alzheimer's disease research paper conclusion

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Alzheimer Disease: Standard of Diagnosis, Treatment, Care, and Prevention

Affiliations.

1 Department of Psychosomatic Medicine, University Medical Center Rostock, Rostock, Germany; [email protected].
2 Deutsches Zentrum für Neurodegenerative Erkrankungen, Rostock/Greifswald, Rostock, Germany.
3 Department of Neurology, Downstate Health Sciences University, State University of New York Brooklyn, New York, New York.
4 Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
5 Lund University, Clinical Memory Research Unit, Lund, Sweden.
6 Department of Physiology and Pharmacology "Vittorio Erspamer," Sapienza University of Rome, Rome, Italy, and San Raffaele Cassino, Cassino, Italy.
7 Department of Neurogenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany.
8 Deutsches Zentrum für Neurodegenerative Erkrankungen, Bonn, Germany.
9 Institute of Social Medicine, Occupational Health and Public Health, Medical Faculty, University of Leipzig, Leipzig, Germany; and.
10 Department of Psychosomatic Medicine, University Medical Center Rostock, Rostock, Germany.
11 Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, China.
PMID: 35145015
PMCID: PMC9258577
DOI: 10.2967/jnumed.121.262239

Alzheimer disease (AD) is the most frequent cause of dementia in people 60 y old or older. This white paper summarizes the current standards of AD diagnosis, treatment, care, and prevention. Cerebrospinal fluid and PET measures of cerebral amyloidosis and tauopathy allow the diagnosis of AD even before dementia (prodromal stage) and provide endpoints for treatments aimed at slowing the AD course. Licensed pharmacologic symptomatic drugs enhance cholinergic pathways and moderate excess of glutamatergic transmission to stabilize cognition. Disease-modifying experimental drugs moderate or remove brain amyloidosis, but so far with modest clinical effects. Nonpharmacologic interventions and a healthy lifestyle (diet, socioaffective inclusion, cognitive stimulation, physical exercise, and others) provide some beneficial effects. Prevention targets mainly modifiable dementia risk factors such as unhealthy lifestyle, cardiovascular-metabolic and sleep-wake cycle abnormalities, and mental disorders. A major challenge for the future is telemonitoring in the real world of these modifiable risk factors.

Keywords: PET; amyloid; biomarkers; dementia; prevention; treatment.

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Imaging features of AD. (A)…

Imaging features of AD. (A) Different neuroimaging profiles of cognitively normal individual and…

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Alzheimer's disease is a progressive neurodegenerative disease that impairs memory and cognitive judgment and is often accompanied by mood swings, disorientation and eventually delirium. It is the most common cause of dementia.

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Immunizing mice against amyloid-β is shown not only to prevent plaques forming, but also to clear away existing deposits.

1984: Identifying amyloid-β in brain plaques

Evidence mounts that these peptides are the key component of plaques associated with Alzheimer’s and might give rise to the disease.

Lisa Heinke

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Seven recent papers amplify advances in Alzheimer’s research

Alzheimer's Disease Biomarkers Dementias Neuroscience

abstract graphic of a brain above a hand

AMP AD uses an open-science research model that makes all data and methods rapidly available to the research community at large through the data sharing infrastructure, the AD Knowledge Portal. Since the Portal’s launch in 2015, more than 3,000 researchers world wide from the academic, biotech, and pharmaceutical industry sectors have used the data resources for research on Alzheimer’s and related dementias.

Alzheimer’s is a complex disease, and as it slowly develops, many normal biological processes in the brain and the body go awry, from inflammation, to blood vessels damage and neuronal death. Seven recent AMP AD reports showcase research advances related to the discovery of new drug candidate targets, identification of molecular subtypes of the disease, and new potential biomarkers that can serve as the basis for a precision medicine approach to therapy development.

Identifying ATP6VA1 gene as a candidate target for treatment: Researchers at the Icahn School of Medicine at Mount Sinai in New York generated several types of molecular data from 364 brain donors at different stages of Alzheimer’s. Using network modeling, a way to show data and its relationships, the team identified large sets of genes associated with the disease. Among the thousands of molecular changes associated with Alzheimer’s, the expression of a set of neuronal genes (neuronal network) was the most disrupted. Their analyses identified ATP6VA1 as a master regulator gene of this neuronal network and demonstrated that increasing its expression genetically, or by using a pharmacologic agent, led to improving neuronal function in cultured cells and in flies. These findings were published in Neuron and pave the way for new drug discovery efforts targeting ATP6VA1 .

Finding and validating VGF gene as key regulator of Alzheimer’s: Another AMP AD study led by researchers at Icahn School of Medicine identified the VGF gene and protein as having a key role in protecting the brain against Alzheimer’s. This discovery was made possible by combining computational analyses that integrate large human Alzheimer’s molecular datasets, clinical features of Alzheimer’s, DNA variation, and data on gene- and protein expression with experimental studies in mouse models. The findings provide a new target for researchers seeking to develop drugs to treat or prevent Alzheimer’s. The report of the discovery of this gene as a key driver and its validation in mouse studies was published in Nature Communications .

Identifying different types of microglia associated with Alzheimer’s: An AMP AD research team at Columbia University conducted a study that measured the expression of genes in individual microglial cells purified from human brain samples obtained at autopsy and during neurosurgical procedures. This single cell profiling technology identified several molecular subtypes of microglia based on the pattern of gene expression. Follow-up validation studies in post mortem brain tissue showed that this microglia subtype was less abundant in Alzheimer's brains compared to control brains. These results, published in Nature Communications , will help design larger, more specific studies of the role of microglia subtypes in Alzheimer’s.

Using data to unfold and predict disease process: An AMP AD team led by researchers at Sage Bionetworks in Seattle used innovative computational approaches to make predictions about the sequence of molecular changes that lead to Alzheimer’s. The team used RNA sequencing data collected from a large collection of post-mortem tissue from Alzheimer’s and control brains. This modeling method, called the manifold learning method, predicted early-stage disease processes, such as RNA-splicing, mitochondrial function, and protein transport. Additionally, the method predicted several distinct molecular subtypes of late-onset Alzheimer’s. These predictions speak to the complex nature of the disease and the need to verify these observations in longitudinal studies where molecular signatures can be linked to different clinical features of the disease. These findings were published in Nature Communications .

Network modeling identifies molecular subtypes of Alzheimer’s: Using a large collection of human brain samples from different studies, a team led by researchers at Icahn School of Medicine also analyzed RNA sequencing data and identified three major molecular subtypes of Alzheimer’s. The subtypes, which are independent of age and disease stage, and are replicated across multiple brain regions, show how different combinations of biological pathways lead to brain degeneration. With further research and validation in larger groups, these molecular subtypes may help reveal how Alzheimer’s progresses and potential ways to slow or stop it. Their findings were published in Science Advances .

Identifying new biomarkers in spinal fluid: AMP AD researchers at Emory University identified groups of proteins (protein panels) associated with Alzheimer’s that could be identified in both brain and spinal fluid. These overlapping protein panels detected in the spinal fluid reflected changes in multiple biological process in the brain. The researchers found this by measuring 3,500 proteins in spinal fluid, and 12,000 proteins in a collection of postmortem brain samples, from patients with Alzheimer’s and cognitively normal study participants. The study also showed that these changes in the protein expression pattern were specific for Alzheimer’s. This work lays the foundation for the discovery of new fluid biomarkers for Alzheimer’s. These findings were published in Science Advances .

Investigating how being female may increase risk of Alzheimer’s: Duke University researchers and members of the Alzheimer’s Disease Metabolomic Consortium (ADMC) participating in the AMP AD program, analyzed the changes in the levels of 180 metabolites in the blood from more than 1,500 people who took part in the NIA-supported Alzheimer’s Disease Neuroimaging Initiative . The researchers reported that there are differences in a subset of blood metabolites associated with Alzheimer's based on sex and ApoE4 status. ApoE4 is the strongest Alzheimer's risk factor gene. Women with Alzheimer’s who carry the ApoE4 gene have a distinct metabolic pattern in blood. These metabolic changes suggest that females have a greater impairment of brain energy production than males. Dissecting metabolic differences in Alzheimer’s can identify specific pathways within specific patient subgroups and guide the way to personalized medicine.

The data and methods from the above studies are available and can be accessed by researchers across the world through the AD Knowledge Portal . The portal is the data repository for the AMP AD Target Discovery Program, and other NIA-supported team-science programs operating under open-science principles. Now in its sixth year, AMP AD is demonstrating the power of open science to enable the scientific community to investigate difficult scientific questions and jumpstart new drug discovery projects.

The AMP AD research teams are funded by NIA grants U01AG046152, U01AG046170, U01AG046139, U01AG046161, R01AG046171, R01AG046174, U19AG010483, U01AG042791, U01AG061357, U01AG061359, U01AG061835, and U24AG061340.

The studies outlined here were also supported by the following NIA grants (in order of appearance):

ATP6VA1: NIA grants U01AG046170, RF1AG054014, RF1AG057440, R01AG057907, U01AG052411, R01AG062355, U01AG058635, and R01AG068030
VGF: NIA grants U01AG046170, R01AG046170, RF1AG054014, RF1AG057440, R01AG057907, R01AG055501, U01AG046161, P50AG025688, 5R01AG053960, and 5R01AG062355
Microglia: NIA grants U01AG046152, R01AG036836, R01AG048015, and RF1AG057473
Disease process: NIA grants U54AG054345, RF1AG057443, P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152, P50AG016574, R01AG032990, U01AG046139, R01AG018023, U01AG006576, U01AG006786, R01 AG025711, R01AG017216, and R01AG003949
Subtypes: U01AG046170, RF1AG054014, RF1AG057440, R01AG057907, U01AG052411, R01AG062355, U01AG058635, R01AG068030, P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152, U01AG52411, K01AG062683, and U01AG058635
Spinal fluid biomarkers: NIA grants R01AG053960, R01AG057911, R01AG061800, RF1AG057471, RF1AG057470, R01AG061800, R01AG057911, R01AG057339, U01AG046161, and U01AG061357
Female risk: NIA grants U01AG024904, P30AG10161, R01AG15819, R01AG17917, U01AG46152, U01AG61356, R01AG059093, R01AG046171, RF1AG051550, and U01AG024904, RF1AG058942, R01AG057452, R03AG054936, and RF1AG061872

These AMP AD activities relate to NIH’s AD+ADRD Research Implementation Milestone 2.A , “Create new research programs that use data-driven, systems-based approaches to integrate the study of fundamental biology of aging with neurobiology of aging and research on neurodegeneration, AD and AD-related dementias to better understand the mechanism(s) of vulnerability and resilience in AD across all levels of biologic complexity (from cellular to population level) and to gain a deeper understanding of the complex biology and integrative physiology of healthy and pathologic brain aging;” Milestone 9.B , "Accelerate the development of the next generation CNS imaging ligands and biofluid molecular signatures targeting a variety of disease processes (neuroinflammation, bioenergetic/metabolic compromise, oxidative stress, synaptic pathology) that can be used as research tools or developed into diagnostic, prognostic, theragnostic or target engagement biomarkers;" and Milestone 9.F , “Initiate studies to develop minimally invasive biomarkers for detection of cerebral amyloidosis, AD and AD-related dementias pathophysiology.”

References:

Wang M, et al. Transformative network modeling of multi-omics data reveals detailed circuits, key regulators, and potential therapeutics for Alzheimer's disease . Neuron . 2021;109(2):257-272.e14. doi:10.1016/j.neuron.2020.11.002.

Beckmann ND, et al. Multiscale causal networks identify VGF as a key regulator of Alzheimer's disease . Nature Communications. 2020;11(1): 3942. doi:10.1038/s41467-020-17405-z.

Olah M, et al. Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer's disease . Nature Communications . 2020;11(1):6129. doi:10.1038/s41467-020-19737-2.

Mukherjee S, et al. Molecular estimation of neurodegeneration pseudotime in older brains . Nature Communications . 2020;11(1):5781. doi:10.1038/s41467-020-19622-y.

Neff RA, et al. Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets . Science Advances . 2021;7(2):eabb5398. doi: 10.1126/sciadv.abb5398.

Higginbotham L, et al. Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease . Science Advances . 2020;6(43):eaaz9360. doi:10.1126/sciadv.aaz9360.

Arnold M, et al. Sex and APOE ε4 genotype modify the Alzheimer's disease serum metabolome . Nature Communications . 2020;11(1): 1148. doi:10.1038/s41467-020-14959-w.

nia.nih.gov

An official website of the National Institutes of Health

A cultural approach to dementia prevention

An Introduction to Alzheimer’s Disease: What is it?

By: Adrianna Fusco

Introduction: Alzheimer’s disease, something we hear about online, in commercials, on news stations, and in many other parts of life. However, we are never told much about Alzheimer’s disease other than the devastating impacts it has. What is Alzheimer’s disease? What are the symptoms or signs to look out for? How does it progress? What causes it? How can it be prevented?

What is it? Alzheimer’s disease is a form of dementia, which is just an umbrella term used to describe loss of memory, language, problem solving, and other thinking abilities. More specifically, Alzheimer’s diseaseis a progressive, neurodegenerative disease that is categorized by a loss of memory, along with basic life skills like eating, bathing, talking, etc.

Symptoms: Common symptoms include: memory loss, paranoia, depression, anger, aggression, anxiety, apathy, loneliness, and psychosis. These symptoms vary from person to person.

Progress: As mentioned above, Alzheimer’s disease is a progressive disease. This means that it develops and gets worse over time. In the first stages of Alzheimer’s disease, there is usually very mild memory loss or problems with thinking abilities. The person may have a hard time remembering where they placed something or have a hard time recalling the right word to say. However, they still are independent, meaning they can still take care of themselves and do things like driving.

During the middle stages of Alzheimer’s disease, the cognitive processes get worse. Now the person may not be able to remember their personal history, like their address or phone number. They also may have a hard time recalling memories or remembering something from their past. The person is no longer able to take care of themselves because in this stage, they tend to forget where they are and often have a hard time using the bathroom or getting dressed appropriately for the day. An example of this is the person wearing shorts in the winter. Along with the cognitive changes, the person may begin to feel sad, lonely, anxious, and paranoid. The symptoms vary from person to person.

When the person hits stage 2, they will need a caregiver to assist them with their tasks and the caregiving will increase as the disease progresses. However, it’s important to help them without trying to do everything for them. They are still adults and they want to be treated as such, so it’s important to still let them have at least some control over their life. Whether that’s letting them do simply chores, like folding clothes, or doing activities, like arts and crafts. This will help provide a sense of normalcy.

The final stage of Alzheimer’s disease is when people begin to lose sense and control of the environment around them. By this point, the cognitive abilities of the individual have tremendously decreased. They can no longer speak in long formulated sentences, instead they speak in short fragments or words. They have trouble completing everyday tasks like walking, sitting, eating, and drinking. This means that they require around the clock assistance to make sure that they are remembering to eat and to help them eat. In general, the assistance is meant to make sure the person is safe and is living to their best ability. At this point, the individuals are very susceptible to infections. When the symptoms and daily conditions get really bad, usually, families turn to hospice care, so that the patient is comfortable at the end of their life. Hospice care also provides emotional support to loved ones, which is vital. Losing a loved one can cause serious emotional and mental strain, so that support is important.

The cause of Alzheimer’s disease is still being researched, but researchers have identified what they believe to be the main culprits of the disease: plaques and tangles.

Plaques are deposits of amyloid beta that forms between nerve cells that blocks the signals and stops the right materials from being sent to the nerve for survival. In a healthy brain, amyloid beta is used to help support neural repair and growth. However, in Alzheimer’s disease, there is an overproduction of this amyloid beta protein that disturbs these cells and eventually causes the death of the cells. The death of the old cells causes the loss of old memories and information. The blocking of nerve cells can stop the production of new connections, which means short term memories are not being accurately encoded in the brain to become long term memories.

Tangles are made up of twisted tau that builds up between cells. In a healthy brain, tau is used to help support neural strength and is important in keeping stability in the cells. However, a build up leads to the cells not being able to receive signals and the supplies it needs to function (i.e. energy). These lead to death of the cells, leading to loss of information and life skills.

There is also a biomarker known as APOE-4, that is thought to predispose people to Alzheimer’s disease. This gene along with some environmental stressors could affect whether someone gets the disease and the progression of it. However, a lot of research is still being conducted on this topic and we are constantly rerouting what we know, as new information is found.

Alzheimer’s disease is a terrible disease that claims the lives of a lot of people every year. It’s important to know the signs and to check up with your doctor when anything seems unusual. Alzheimer’s disease and dementia are not a normal part of aging, so see your doctor if you notice any issues with your memory. The earlier the disease is detected, the better it can be treated.

Stay tuned for more blog posts about Alzheimer’s disease, including a look into the mental health of caregivers, prevention, treatment, and more! We also will be writing posts about interviews with doctors, as well as posts about brain health!

Thank you for reading!

References:

“Alzheimer’s Caregivers: 8 Tips for People Caring for a Loved One With Alzheimer’s Disease or Dementia: Caregivers.” 30Seconds Health ,

30seconds.com/health/tip/14389/Alzheimers-Caregivers-8-Tips-for-People-Caring-for-a-Loved-One-With -Alzheimers-Disease-or-Dementia.

Mayeux, Richard, et al. “Treatment of Alzheimer’s Disease: NEJM.” Edited by Alastair J.J. Wood, New England Journal of Medicine , 16 Mar. 2000, www.nejm.org/doi/pdf/10.1056/NEJM199911253412207.

NHS Choices, NHS, 10 May 2018,

www.nhs.uk/conditions/alzheimers-disease/causes/#:~:text=Alzheimer’s%20disease%20is%20thought%2 0to,form%20tangles%20within%20brain%20cells.

Porsteinsson, Anton P., et al. “Neuropsychiatric Symptoms in Dementia: A Cause or Consequence?” American Journal of Psychiatry , American Psychiatric Association Publishing, 30 Apr. 2015, ajp.psychiatryonline.org/doi/10.1176/appi.ajp.2015.15030277#:~:text=The%20term%20neuropsychiatric %20symptoms%20describes%20heterogeneous%20behavioral%20or,agitation%2C%20anxiety%2C%20 apathy%2C%20depression%2C%20psychosis%2C%20and%20sleep%20disturbance.

“Stages of Alzheimer’s.” Alzheimer’s Disease and Dementia , www.alz.org/alzheimers-dementia/stages.

“What Is Alzheimer’s?” Alzheimer’s Disease and Dementia ,

www.alz.org/alzheimers-dementia/what-is-alzheimers.

Open access
Published: 17 November 2022

Early prediction of Alzheimer's disease using convolutional neural network: a review

Vijeeta Patil 1 ,
Manohar Madgi ORCID: orcid.org/0000-0001-5118-846X 1 &
Ajmeera Kiran 2

The Egyptian Journal of Neurology, Psychiatry and Neurosurgery volume 58 , Article number: 130 ( 2022 ) Cite this article

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In this paper, a comprehensive review on Alzheimer's disease (AD) is carried out, and an exploration of the two machine learning (ML) methods that help to identify the disease in its initial stages. Alzheimer's disease is a neurocognitive disorder occurring in people in their early onset. This disease causes the person to suffer from memory loss, unusual behavior, and language problems. Early detection is essential for developing more advanced treatments for AD. Machine learning (ML), a subfield of Artificial Intelligence (AI), uses various probabilistic and optimization techniques to help computers learn from huge and complicated data sets. To diagnose AD in its early stages, researchers generally use machine learning. The survey provides a broad overview of current research in this field and analyses the classification methods used by researchers working with ADNI data sets. It discusses essential research topics such as the data sets used, the evaluation measures employed, and the machine learning methods used. Our presentation suggests a model that helps better understand current work and highlights the challenges and opportunities for innovative and useful research. The study shows which machine learning method holds best for the ADNI data set. Therefore, the focus is given to two methods: the 18-layer convolutional network and the 3D convolutional network. Hence, CNNs with multi-layered fetch more accurate results as compared to 3D CNN. The work also contributes to the use of the ADNI data set, where the classification of training and testing samples is divided with such a number that brings the highest accuracy achieved with 18-layer CNN. The work concentrates on the early prediction of Alzheimer's disease with machine learning methods. Thus, the accuracy achieved is 98% for 18-layer CNN.

Introduction

The deterioration of physical and neurological functions in persons is part of the aging process. Although deterioration is natural, it can significantly influence some persons due to certain risk factors. Alzheimer's disease is a neurocognitive disorder occurring in people in their middle or old age, and it affects 46.8 million people globally and can impact a person's quality of life [ 1 ]. AD populations are estimated to increase to 106.8 million by 2050 [ 2 , 3 ]. The estimated cost of long-term health care for dementia patients is about $290 billion [ 4 ]. Research toward early AD diagnosis is ongoing to slow down the abnormal degradation of neurons in the brain. It also produces emotional and financial benefits for the patient family [ 5 ]. This disease causes the person to suffer from memory loss, unusual behavior, and language problems. It is caused due to the tangled bundles of neurofibrillary fibres of the brain and certain regions of the brain like the entorhinal cortex and hippocampus [ 6 ]. The initial symptoms, such as episodic memory impairment and the navigational problem of the patient, are typical variants. The higher order symptoms include memory loss, impaired judgment, difficulty in identifying objects, confusion in paying bills and driving a vehicle, and placing objects in odd places.

Alzheimer's disease is divided into three periods: the primary period, the intermediate period, and the last period of dementia. AD is diagnosed through the brain monitoring modalities, such as CT (Computer Tomography) scan and PET (Positron Emission Tomography) scan resting-state functional magnetic resonance imaging (RS-FMRI) [ 7 ].

AD is a neurodegenerative disease with symptoms, such as motor dysfunctions of the body. The results of this work created a strong link between inflammation and neurotoxic kynurenines of human samples. A need for biomarkers is necessary due to chronic low-grade inflammation [ 8 ].

The dissection of neuroprotective and neurodegenerative components of AD-affected areas in the brain. It focuses on etiology, pathomechanism, biomarkers, Imaging techniques, and novel therapeutic targets of Alzheimer's disease [ 9 ].

The EEG biomarkers help predict clinical outcomes in patients regularly. The accuracy of psychophysiological biomarkers based on EEG while predicting the outcome of the patients. The machine learning technique reached an accuracy of 83.3%, with EEG-based functional connectivity predicting clinical outcomes in nontraumatic patients [ 10 ].

The neurovisceral integration model of fear is being used. That is, A richer understanding of neurovisceral concomitants of this function has both theoretical and clinical implications [ 11 ].

Treatment of fear-related disorders occurs due to neuro disorder AD. To fix this, research has been done using a novel frequency domain analysis of heart rate using a short-time Fourier transform from a point process modeling algorithm [ 12 ].

Pavlovian and Instrumental learning can be integrated to guide behavior in a phenomenon experimentally known as Pavlovian-to-Instrumental Transfer (PIT) to investigate numerical applications in clinical contexts such as working memory affected due to AD [ 13 ].

Mitochondrial DNA is identified as an inheritable metabolic disease with neurological manifestation and pathogenesis of illness, including neurodegenerative diseases, such as Alzheimer's disease [ 14 ].

Kynurenic acid (KYNA) is an endogenous tryptophan (Trp) metabolite with neuroprotective properties. KYNA plays critical roles in nociception, neurodegeneration, and neuroinflammation. A lower level of KYNA is observed in patients with neurodegenerative diseases, such as Alzheimer's [ 15 ].

Machine learning

Understanding machine learning and the standard machine learning approaches used in AD prognosis is necessary before starting the deeper examination of machine learning methodologies. Artificial intelligence includes machine learning, which contains various tools for making probabilistic and statistical judgments based on prior knowledge. Classifying new events and forecasting new patterns depends on prior learning (training). When compared to standard statistical methods, machine learning is much more powerful. For machine learning to be successful, it is essential to have a good understanding of the problem and the algorithms' constraints. As a result, it has a fair chance of success if experimentation is carried out appropriately, training is used effectively, and outcomes are rigorously validated.

This paper reviews the state-of-the-art techniques and data sets used to detect Alzheimer's disease. The various researchers' work, different classifiers used to detect Alzheimer's disease early, and the results obtained are discussed. A literature survey is conducted to know every possibility is explored to detect the initial stages of AD using the ML approach. This survey includes a list of methodologies, data sets, and accuracy gained. The study exhibits the most appropriate strategy for quick treatment of AD based on studies conducted from 2016 to 2021.

Mehmood et al. [ 1 ] stated that identifying Alzheimer's on magnetic resonance images in the initial period is carried out using mild cognitive impairment detection using the tissue segmentation of the brain with the help of multiple layers called structured deep learning. The study uses Visual Geometry Group architecture belonging to deep convolutional neural network architecture. The FMRI images used in this paper are gathered from ADNI (Alzheimer's disease neuroimaging initiative) and are found online at adni.loni.usc.edu. To analyze and verify the development of MCI, i.e., mild cognitive impairment, different biomarkers such as structural MRI, PET, and MRI were analyzed and authenticated to detect the traces of AD. 300 MRI subjects were considered and further classified as Alzheimer's, late mild cognitive, and initial mild cognitive periods. The techniques used in this study are CNN with a multi-layered form of various layers, such as convolution layer, pooling layer, and softmax layer. An accuracy of 98.73% is achieved using multi-layered CNN without data augmentation.

Odusami et al. [ 16 ] proposes a deep learning method to detect the early stage of AD. He has proposed a modified ResNet18 model for extracting the features of neuroimaging data from structural magnetic resonance imaging. The data set is fetched from ADNI accessed on January 2021. The data sets are available in DICOMM file format. A total of 413 subjects were considered for the study. The six categories of the database are normal healthy period, light cognitive inability EMCI and notable remembrance, and Alzheimer's is addressed. The techniques used are residual network with 18 layers CNN is proposed. It uses a 3 × 3 seiver, and the phase 1 pooling layer has a 1 × 1 seiver, a completely interlinked, and a softmax layer. The fine-tuned CNN of 18 layered neural networks obtained a separation rate of accuracy of about 99.09%. CNNs are used to detect active magnetic resonance imaging scanned sheets of Alzheimer affected persons. The process is carried out in data collection, preprocessing and fine-tuning, and classification and evaluation stages.

Venugopalan et al. [ 17 ] proposed removing noise from MRI scans using automatic encoders for evoking properties from given data. He has stated a novel method of 3D CNN for imaging data, and he concentrated on the hippocampus brain area and features. Audio oral tests are extracted. The ADNI data set is used considering biological markers MRI, PET, and neuropsychological assessments to measure the progression of mild cognitive impairment. The cross-section Magnetic Resonance scan image gathered about 8209 voxels scattered in 18 parts. A total of 220 patients were considered for the test. The total count of MRI images is 503 in number, SNP is 808 in number, and HER is 2004. A three-tier automatic encoder is used with 199,99, and 51 nodes separately for every part. The first step is to filter noise, and the next is to extract 1680 common features and convert input data into 0's and 1's format by shot encoding. An accuracy of 78% is achieved.

Pradhan et al. [ 18 ] proposed the detection of different stages of AD. The method used is VGG19 and DenseNet169 architecture for classification. The data set is taken from an open online data set library called Kaggle. There are 6000 images labeled as mild, moderate, very mild, and non-demented AD. The features are considered for 80% of learning and 20% of examining phases. VGG19 has around 10–16 convolutional neural network layers. For image classification, DenseNet is used. Here, VGG19 performs better than DenseNet accuracy of 94% is achieved.

Shah et al. [ 19 ] hard and soft voting algorithms were implemented to classify and identify the initial AD period. The data set consists of 437 patients aged between 60 and 96. Among these, 72 people are non-demented, 64 are demented, 70% are used to train the algorithm, and 30% are used to test the algorithm. The classification algorithms are hard voting, soft voting classifiers, and decision tree. SVM is used as a classification method. An accuracy of 84% is obtained for the voting classifier algorithm.

Huanhuan et al. [ 20 ] proposed detecting early stages of dementia (ConvNets) with the help of MRI. The classification of scan images is done using gray color regions and white color regions in the scanned images of the brain. The data are collected from the ADNI database. The number of MRI images collected is 615 in number. The data are segregated in the proportion of 3:1:1. Statistical parameter mapping is used in the preprocessing stage to reduce the patient's head movement, and images are reduced to size 192 × 192 × 160. The techniques used for the detection are based on classifiers eResidual Network of 50 layers, eNeural Architecture Search Network. Adding a dropout layer addresses the overfitting problem to the fully interconnected layer. The accuracy rates are separately obtained from around 97.65% to 88.37% for MCI AD.

Razavi et al. [ 21 ] highlighted using unsupervised feature learning, which has two steps. The first step is to extract features from the raw data. The methods used are scattered filtering and uncontrolled neural layer network. Sparse filtering and regression are called softmax to classify healthy and unhealthy persons. A few unsupervised learning techniques, such as Boltzman machines and dispersed coding, are used to distribute collected data. The data set used in this method is ADNI with cerebrospinal fluids. The total number of AD patients is 51, and 43 patients have mild traces of suffering from AD. The MRI data were obtained using 1.5T scanners. The highest accuracy obtained is 98.3% while using the softmax regression.

Islam et al. [ 22 ] work on AD uses deep learning CNN to analyze brain MRI images. This work also identifies the different stages of the disease. The method also works well with the imbalanced data set. The CNN uses four layers: deep neural layers, batch processing layer, pooling layer, and ReLU layer. According to the 3D brain, MRI data architecture, inception -v4, and Resnet classify the data. The data set used in OASIS has 416 data samples. The training and testing data set is divided into the 4:1 proportion. The performance rates of inception -v4 and Resnet precision rates are 0.81 and 0.82, respectively.

Islam et al. [ 23 ] stated that 3D convolutional neural networks work better in visualizing medical images. Brain PET scans are used to detect Alzheimer's disease using 3D CNN, and five visualization techniques are applied. The data set is collected from ADNI (adni.loni.usc.edu). A total of 1230 PET scans of AD patients are available. The applied visualization techniques are guided by Backpropagation Brain area Occlusion and layerwise relevance propagation. 80% of the data set is used for training, 20% for testing, and the remaining 10% for validation. The visualization techniques are used to enhance and focus on the regions of the brain, such as the frontal mid, precuneus, postcentral, temporal mid, and precentral areas. Hence, the system achieved an efficient classification accuracy of 88.76% is achieved.

Thakare et al. [ 24 ] stated using EEG to detect Alzheimer's disease. The EEG database is extracted from Kashi Bhai Hospital, Pune, and nineteen numbered channels of the EEG database. First, the patients are diagnosed with a clinical diagnosis of MSME. Based on this, the patients are divided into healthy and AD patients. The EEG signals obtained are converted into a.mat file, and the acquisition is made using Simulink. The features extracted from these EEG waves are mean, standard deviation, and mode using wavelet transforms. The classification uses a support vector machine and a normalized minimum distance (NMD) classifier algorithm. An accuracy of 95% is achieved using SVM holds good as compared to the NMD classifier.

Noor et al. [ 25 ] the most popular DL techniques have been explored in detecting those three leading neurological disorders from the MRI scan data. DL methods for the classification of neurological disorders found in the literature have been outlined. The pros, cons, and performance of these DL techniques for the neuroimaging data have been summarized. Prime observation of this study included the maximum usage of CNN in the detection of Alzheimer's disease and Parkinson's disease. On the other hand, DNN has been used with greater prevalence for schizophrenia detection.

Su et al. [ 26 ] Magnetoencephalography (MEG) has been combined with machine learning techniques to recognize Alzheimer's disease (AD), one of the most common forms of dementia. A bimodal recognition system based on an improved score-level fusion approach is proposed to reinforce the interpretation of the brain activity captured by magnetometers and gradiometers. This preliminary study found that the markers derived from the gradiometer tend to outperform the magnetometer-based markers. Interestingly, out of the ten regions of interest, the left-frontal lobe demonstrates about 8% higher mean recognition rate than the second-best performing region (left temporal lobe) for AD/MCI/HC classification.

In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases, such as Alzheimer's disease. Have presented a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models [ 27 ].

This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for AD classification [ 28 ].

Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer's disease diagnosis in clinical research. Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people. The proposed network can be very beneficial for early stage AD diagnosis. Though the proposed model has been tested only on the AD data set, we believe it can be used successfully for other classification problems in the medical domain [ 29 , 30 ].

A convolutional neural network with multi-layers such as pooling, softmax regression, and completely interconnected layers is used to detect the disease. A CNN increases the size of the images in length and breadth while decreasing the complexity of the image. A pooling process reduces the overfitting problem as the amount of computation and parameters are reduced. The transfer learning model with customized VGG architecture is used to get the highest accuracy rates [ 1 ]: the data collection, preprocessing, fine-tuning, and classification stages. Fine-tuning is used to reduce errors with the help of ImageNet. It uses the residual network with optimal parameters ReLU and stochastic gradient descent. A novel deep learning method with shallow models for integrating data and autoencoders in a minimal data set. VGG 19 of 16 convolutional layers when a large data set is available to classify, and the dense net is utilized to reduce the number of parameters [ 17 ].

DenseNet 169 is used for image classification. Both models are compared; VGG 19 performs better than DenseNet [ 18 ]. Used the support vector machines for classification and used hard and soft voting classifiers to get the optimum accuracy and use of decision trees for regression, making the system fast and efficient in predicting the missing values of the field [ 19 ]. ConvNet for classification and ensemble machine learning techniques are used for a final product from CNN layers. Backpropagation networks either increase or decrease weights to match output with input. Bernoulli's function is used to avoid the overfitting problem [ 20 ]. Sparse filtering and softmax regression are trained automatically to identify healthy and unhealthy individuals. These two methods are called the two-stage learning method [ 21 ]. These are used for deep convolutional neural networks with four functions, pooling, convolution, batch normalization, and rectified linear unit [ 22 ]. The techniques such as 3D convolutional neural networks and visualization techniques include layerwise relevance propagation, guided backpropagation, and sensitivity analysis to detect AD [ 23 ]. Proposed the use of support vector machines and normalized minimum distance classifier, and it uses a supervised learning model for better results [ 24 ]. Table 1 illustrates the list of methodologies.

Comparison of 18-layer CNN and 3D CNN

The two main components of the CNN architecture are a toolkit that analyses and identifies the properties of the image with a process called feature extraction and a second component based on the prediction process, which estimates the image category from the previous stages. A total of five layers are used: CNN layer, max-pooling layer, completely inter-connected layer, activation layer, and dropout layer. This set of five layers is expanded with approximately 240 filters, each of size 5 × 5. The input for these CNN layers is FMRI image, Pet, and CT scan images that undergo all the preprocessing and conversion processes in the proposed methodologies. In the case of 18-layered CNN, the model predicts the output with the highest accuracy as it has to pass through all the bitwise filters. Hence, with 3D CNN networks, the detection of AD disease might be restricted to less accuracy than 26-layered CNN. Detecting damaged neurofibrils in the brain is easily verified with the multi-layered CNN. In the survey of these related works, maximum use of CNN is being done. Figure 1 represents the comparison of multi-layered CNN versus 3D CNN.

Multiple layers convolution neural network architecture versus 3D convolution neural network architecture

The data set used in the following papers is shown in Table 2 , along with the description data set source, the total number of samples used, the count of training and testing samples, and the number of NC—Normal Control, LMCI—Late Mild Cognitive Impairment, EMCI—Early Mild Cognitive Impairment, and Alzheimer's disease.

This section illustrates the results and outcomes of various works shown in Tables 3 , 4 , and 5 and Figs. 2 and 3 .

Comparison of accuracies with different CNNs

Summary of results found in research studies

According to the different works examined, the detection of AD carried out using ResNET18 networks holds the highest accuracy of 98% conducted using seven binary classifications by comparing NC, EMCI, LMCI, and AD. This technique yields efficient accuracy, sensitivity, and specificity results, as considered in previous studies. Tables 4 and 5 list the accuracies from different ML models, such as voting classifiers, decision tree classifiers, SVM, and XG boost algorithms. Table 5 gives the type of CNN network, such as 18-layered CNN with the highest accuracy of 98% and 3D CNN network accuracy 0f 88% to detect Alzheimer's disease.

Early detection of Alzheimer's disease combined with proper cognitive stimulation can reduce the impact on older people and their families. To diagnose this disease, Artificial Intelligence is a study utilized for the early detection of disease in the very first stage. The two most important machine learning algorithms, 18-layered Convolutional Neural Network (CNN) and 3D CNN are used to identify preliminary periods of Alzheimer's disease, implemented on MRI and CT scans and brain monitoring modalities [ 20 ]. The ADNI data set is preferred, and a comparison is made between 18-layered CNN and 3D CNN, focussing on neural networks yielding better results [ 22 , 23 ]. The work illustrates that the best suitable algorithm is 18-layered CNN with an accuracy of 98%, thereby reducing the manual work of the radiologist [ 16 ].

Limitations and future directions

The convolutional neural networks limit the complete detection of AD in the initial stage of the disease. The multi-layered CNN becomes more complex while identifying the affected areas of the brain in old age people. The CNNs do not work with the loss of memory of the patient as there are no signs of it in the sensitive regions of the brain. In the future, the same set of CNNs can also be used parallelly to detect other neurogenerative diseases, such as Parkinson's disease. In future work, the different sets of features can be extracted, and redundant features can be filtered through a convolutional neural network to detect Alzheimer's disease in the seed stage.

This paper compares and evaluates recent research on machine learning techniques for Alzheimer's disease prognosis and prediction. The most recent developments in machine learning have been exposed, including the types of data employed and the effectiveness of machine learning techniques in diagnosing Alzheimer's in its early stages. Machine learning inevitably increases prediction accuracy, especially compared to standard statistical methods. Accuracy resulted in 80–98% using different convolutional neural networks and 3D CNN. The represented methods did not classify the data set as NC, EMCI, and LMCI but considered the local database from Pune hospital of EEG data set for study. In the proposed models, voting classifiers are preferred in monumental state examinations, and clinical counseling is considered. The data set considered in this model is only right-handed people aged between 60 and 96. The data set's classification is not based on the stages of the disease. However, 80% of training and 20% of testing data are distributed and use the DenseNet model and VGG19 architecture, which is why the accuracy reduction by around 87%. The non-classification of a data set based on stages of the disease is the disadvantage of obtaining the lowest accuracy. Using a convolutional neural network with more than 15 layers is best considered for the highest accuracy rate in work as compared to 3D convolutional neural networks.

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Abbreviations

Ambient Assisted Living

Alzheimer's Disease

Artificial Intelligence

Alzheimer's disease neuroimaging ınitiative

Convolutional neural network

Computed Tomography

Digital Imaging and Communications in Medicine

Electroencephalogram

Early mild cognitive ımpairment

Functional magnetic resonance ımaging

Mild cognitive ımpairment

Machine Learning

Magnetic resonance ımage

Mini‐mental state examination

Normal Control

Late Mild Cognitive Impairment

Normalized minimum distance

Open Access Series of Imaging Studies

Positron Emission Tomography

Resting-state functional magnetic resonance imaging

Single-Nucleotide Polymorphisms

Support vector machine

Visual Geometry Group

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We would like to express our thanks to Dr. Basavaraj Anami, Registrar, KLE Technological University, Hubballi for his valuable suggestions.

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Patil, V., Madgi, M. & Kiran, A. Early prediction of Alzheimer's disease using convolutional neural network: a review. Egypt J Neurol Psychiatry Neurosurg 58 , 130 (2022). https://doi.org/10.1186/s41983-022-00571-w

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129 Alzheimer’s Disease Essay Topics & Examples

If you’re writing about patients with memory loss or dementia care and treatment, this article will be of use. Our team has prepared Alzheimer’s disease essay examples and topics below.

🏆 Best Alzheimer’s Disease Essay Examples & Topics

💡 most interesting alzheimer’s disease topics to write about, 📌 simple & easy alzheimer’s disease research topics, 👍 good research topics about alzheimer’s disease, ❓ research questions about alzheimer’s disease.

Pathophysiology of Alzheimer’s Disease The study will discuss the pathophysiology of Alzheimer’s disease, such as risk factors, cellular involvement, genetic influences, and the interventions of the available therapy’s pharmacological Interventions.
Therapeutic Dogs, Dementia, Alzheimer’s and Fluid Intelligence It is worth noting that with dementia, the patient has a speech disorder and a personality change in the early stages of the pathology.
The Alzheimer’s Association Dementia Care Practice Therefore, achieving the philosophy and recommendations of the association is a shared responsibility between doctors, patients, and caregivers. Ultimately, CAPD tests the functionalities of the patient ranging from the psychomotor activities, perceptions, awareness, and orientations, […]
Dementia, Alzheimer, and Delirium in an Elderly Woman Additionally, she struggles with identifying the appropriate words to use in dialogue and changes the topic. Timing: While in the middle of conversations and public places like supermarkets.
Alzheimer’s Disease Diagnosis and Intervention The accumulation of plaques and tangles in the brain is a hallmark of the disease, resulting in the death of neurons and a decline in mental capacity.
Alzheimer’s Disease: Assessment and Intervention The caregiver is recommended to install safety locks and alarms on all doors and windows to prevent the patient from leaving the apartment without supervision.
Management of a Patient With Alzheimer’s: Case Study The correlation between this issue and the probability of the emergence of AD in elderly citizens is proved by the scholars who examined the impact of the quality of air on a person’s health.
Bilinguals’ Cognitive-Linguistic Abilities and Alzheimer’s Disease This irregularity is reflected in the preserved linguistic abilities, including code-switching and semantic fluency, and the declined functions in translation, picture naming, and phonemic fluency, calling for improved therapy and testing practices.
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Predicting biological activity and design of 5-HT 6 antagonists through assessment of ANN-QSAR models in the context of Alzheimer’s disease

Original Paper
Published: 26 September 2024
Volume 30 , article number 350 , ( 2024 )

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Daniel S. de Sousa 1 ,
Aldineia P. da Silva 2 ,
Laise P. A. Chiari 1 ,
Rafaela M. de Angelo 3 ,
Alexsandro G. de Sousa 4 ,
Kathia M. Honorio 2 , 3 &
Albérico B. F. da Silva 1

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Alzheimer’s disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT 6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT 6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT 6 receptor’s functions can be effectively modulated, leading to potential improvements in cognition and memory.

Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks–Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT 6 ) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD.

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Computational Modeling of Drugs for Alzheimer’s Disease: Design of Serotonin 5-HT6 Antagonists

Drug design of new 5-HT 6 antagonists: a QSAR study of arylsulfonamide derivatives

Computational and pharmacokinetics studies of 1,3-dimethylbenzimidazolinone analogues of new proposed agent against Alzheimer's disease

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No datasets were generated or analyzed during the current study.

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This study was partly financed by the National Council for Scientific and Technological (CNPq) (Grants 140894/2021-7 and 152105/2022-0). The authors gratefully acknowledge support from FAPESP. The research was carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI), which was funded by FAPESP (grant 2013/07375-0).

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São Carlos Institute of Chemistry, University of São Paulo, São Carlos, 13560-970, São Paulo, Brazil

Daniel S. de Sousa, Laise P. A. Chiari & Albérico B. F. da Silva

School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 03828-000, Brazil

Aldineia P. da Silva & Kathia M. Honorio

Center for Natural Sciences and Humanities, Federal University of ABC, Santo André, 09210-580, São Paulo, Brazil

Rafaela M. de Angelo & Kathia M. Honorio

Department of Exact and Natural Sciences, Southwest Bahia State University, Itapetinga, 45700-000, Bahia, Brazil

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Contributions

Conceptualization: D.S.S., A.P.S., and A.B.F.S. Data extraction: L.P.A.C. DFT calculations: D.S.S., A.P.S., and R.M.A. Electronic properties: D.S.S., A.P.S., and L.P.A.C. Descriptors calculations: R.M.A. ANN-QSAR building: A.P.S. Molecular planning: D.S.S. Genetic algorithm selection: D.S.S. and A.P.S. Editing: D.S.S., A.P.S., and A.B.F.S. Supervision: K.M.H., A.G.S., and A.B.F.S. Writing—original draft preparation: D.S.S. and A.P.S. Figures: D.S.S. and A.P.S. Project administration: A.B.F.S., K.M.H., and A.G.S. Final version, D.S.S., A.P.S., and A.B.F.S. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Daniel S. de Sousa , Aldineia P. da Silva or Albérico B. F. da Silva .

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de Sousa, D.S., da Silva, A.P., Chiari, L.P.A. et al. Predicting biological activity and design of 5-HT 6 antagonists through assessment of ANN-QSAR models in the context of Alzheimer’s disease. J Mol Model 30 , 350 (2024). https://doi.org/10.1007/s00894-024-06134-5

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Published : 26 September 2024

DOI : https://doi.org/10.1007/s00894-024-06134-5

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X-Chromosome-Wide Association Data Spot Alzheimer’s Loci

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25 Sep 2024

About two-thirds of people with Alzheimer’s disease are women, yet due to the challenges in studying X chromosomes, a genetic foundation for this has been difficult to uncover. Now, two large X-chromosome-wide association studies (XWAS) and a smaller one based on pathologically confirmed AD identify 12 loci that might shed some light on the matter.

One study included data from more than 1 million people.
It pegged a variant in SLC9A7 that boosts AD risk by 5 percent.
The variant may ramp up Aβ production by disrupting pH in the Golgi.

In the September 9 JAMA Neurology, scientists led by Michael Belloy, Washington University in St. Louis, report four common and two rare variants on the X chromosome that associate with AD. This follows an XWAS uploaded to medRxiv May 3, in which an international group led by Céline Bellenguez, University of Lille, France, identified four different loci, and a paper published September 4 in Translational Psychiatry describing two more that were uncovered by Valentina Escott-Price, Cardiff University, U.K., and her colleagues.

Of these 12 loci, only one, near the SLC9A7 gene, reached genome-wide significance. The other 11 were significant at the chromosome-wide level and warrant further study, scientists say. In association studies, scientist consider variants that pass genome-wide significance as “hits,” and those that pass the less-stringent chromosome-wide test as “suggestive.”

“The new XWASes and other X chromosome studies are pivotal because they could pave the way for new therapeutic targets that benefit men, women, or both sexes,” wrote Dena Dubal of the University of California, San Francisco, who was not involved in any of the studies.

The X chromosome contains about 1,200 genes. “This is 5 percent of the genome that has been dark to us,” Belloy told Alzforum. This chromosome is typically omitted from GWAS because it complicates statistical analyses. In women, most genes on one of these two chromosomes are inactivated during early embryonic development to avoid double-dosing. Some genes escape this inactivation, however, and this is unpredictable and can change during life (see Peeters et al., 2023 , for a review).

Belloy and his colleagues parsed the genomes of more than 1,100,000 people, including women and men with AD, their first-degree relatives, as well as healthy controls, to look for variants that associate with AD. The dataset, which included cohorts from the Alzheimer’s Disease Genetics Consortium, Alzheimer’s Disease Sequencing Project, U.K. Biobank (UKB), Finnish health registry (FinnGen), and U.S. Million Veterans Program (MVP), yielded six independent genetic variants—four could be tied to the regulation of nearby genes.

Of the six, one, a single-nucleotide variation in an intron of SLC9A7 stood out. The variant not only exceeded the statistical significance threshold for genome-wide association, but, mechanistically, the SLC9A7 gene might promote Aβ production.

X Factors. Manhattan plot of XWAS meta-analysis shows two rare and four common variants linked to AD. SLC9A7 passed a conservative genome-wide significance threshold (upper horizontal line). The others passed the threshold for X-chromosome-wide significance, a less stringent bar (lower line). [Courtesy of Belloy et al., 2024 . ]

Previously, scientists led by coauthor Joachim Herz at the University of Texas Southwestern Medical Center in Dallas had reported that greater expression of the sodium-hydrogen exchanger NHE6, encoded by the SLC9A7 ortholog SLC9A6, triggers ApoE aggregation, which in turn leads to Aβ accumulation (see Jul 2021 news ; Pohlkamp et al., 2021 ). NHEs function in cellular homeostasis. Blocking NHE6 restored acidification of the early endosomal compartment and suppressed amyloid accumulation, Herz found.

Both SLC9A6 and SLC9A7 are highly conserved across species. “Although their functions aren't exactly the same, they are closely related,” Belloy said. These exchangers regulate the pH and ion levels in their respective cellular compartments—early endosomes for NHE6, the Golgi for NHE7. The researchers suspect that an increase in NHE7 would disrupt the pH in the Golgi compartment and that this, too, could lead to amyloidosis.

How big an effect might the SLC9A7 variant have on AD risk? Because X chromosome inactivation complicates the analyses, Belloy and colleagues came up with a statistical method to account for it. This model suggested that an active SLC9A7 risk variant could nudge up expression of the gene in brain tissue, but only by 16 to 44 percent. Though this seems small, the clinical relevance may be greater, Belloy said. “We think that in early life, you can tolerate a small difference in expression of this gene, but as you age, that becomes more of an issue and can contribute to a build-up of amyloid and tau pathology,” he said. The variant associated with AD in both women and men.

Also X inactivated in women is MTM1, a locus that just missed genome-wide statistical significance. In contrast, the four other loci that passed X chromosome-wide significance—NLGN4X, MID1, ZNF280C, and ADGRG4—appear to escape X inactivation. Therefore, they are likely to affect women more than men.

Bellenguez and colleagues analyzed data from 115,841 AD or AD-proxy cases and 613,671 controls that they obtained from the International Genomics of Alzheimer’s Project, European Alzheimer & Dementia Biobank, UKB, and FinnGen. First authors Julie Le Borgne and Lissette Gomez identified four loci that reached X chromosome-wide, though again not genome-wide, significance. Two fell in introns of genes that are X-inactivated, namely FRMPD4 and DMD. The former regulates dendritic spines and neurotransmission and variants in it cause X-linked intellectual disability. Variants in the adjacent gene MSL3, which is not subject to X-inactivation, cause neurodevelopmental delay ( Piard et al., 2018 ; Basilicata et al., 2018 ).

Variants in DMD, which encodes dystrophin, cause Duchenne muscular dystrophy, which mostly affects boys, and about one-third of them also have cognitive impairment. In male mice, DMD mutations may trigger amyloidosis in the prefrontal cortex and hippocampus ( Hayward et al., 2022 ).

Functional effects for the other two loci are difficult to predict, the authors say, since they both are more than 300bp away from the nearest gene, namely NLGN4X and GRIA3. Dubal previously tied GRIA3 expression to cognitive resilience in women, but the locus Borgne and colleagues identified only associated with AD in men ( Davis et al., 2021 ).

AD Loci? Variants near the TBX22 and Haus7 genes reached the chromosome-wide significance threshold in an XWAS meta-analyses of pathologically confirmed AD. [Courtesy of Escott-Price et al., 2024]

Though their study was smaller, Escott-Price and her colleagues included data from 1,970 people who had had pathologically confirmed AD from 1,113 controls. First author Emily Simmonds analyzed datasets from three tissue bank cohorts: Brains for Dementia Research UK; a KRONOS/Tgen dataset derived from 21 National Alzheimer’s Coordinating Center brain banks and the Miami Brain Bank; and a combination of the Religious Orders Study/Memory and Aging Project, Mount Sinai Brain Bank, and Mayo Clinic Brain Bank. The analysis yielded 264,793 SNPs, of which two, at the TBX22 and Haus7 loci, reached chromosome-wide significance in meta-analysis of all three cohorts, while one, NXF5, passed muster in women only. The authors were intrigued by TBX22 and three other candidate genes, DDX53, IL1RAPL1, and SH3BGRL because all four turned up in separate analysis of at least two of the three cohorts.

Anomalies in three of these genes have previously been reported in various AD models, including elevated expression of DDX53, suppression of IL1RAPL1, and evidence of both in SH3BGRL. NXF5 deficiency has been linked to intellectual disabilities ( Callaerts-Vegh et al., 2015 ).

“Collectively, these studies highlight our emerging and high value for the X chromosome as a contributor to neural-related functions and as a source of sex difference,” Dubal said.

Both Belloy and Bellenguez suspect that variants in genes that escape inactivation help explain the sex differences in AD seen clinically. Women, for example, have less cognitive resilience to AD pathology, as determined by amyloid PET, than men. And yet, they have greater brain resilience to the effects of tangles than men (see Arenaza-Urquijo et al., 2024 , for a review).

Teasing out such effects with XWAS alone may be difficult. Other X chromosome-related factors may also play a role in AD, including epigenetic alterations of the X, genomic imprinting, and X-linked proteomic signatures, noted Rachel Buckley and Mabel Seto, Brigham & Women’s Hospital, Boston, in a JAMA Neurology editorial. “None of these components are effectively captured by XWAS,” they wrote.

Belloy agreed that these phenomena are grist for future analyses. “Our study will allow us to really start exploring how the X chromosome may be implicated in sex differences in Alzheimer’s disease,” he said. “This was a first step.”—Kristel Tjandra

Posted: 26 Sep 2024

In their timely and well-executed study, Belloy et al. conducted a large-scale X-chromosome-wide association study of the genetics of Alzheimer’s disease. Their approach is novel and much-anticipated because the X chromosome has been largely excluded in genome-wide studies due to technical challenges, despite the high relevance of X-linked genes in the brain and in neurological conditions. Those technical challenges have been related to X hemizygosity in male individuals, random X inactivation and baseline X escape in female individuals, shared sequences between the X and Y, and limited representation of the X on SNP arrays. Expanding tool kits in genome-wide association studies, alongside other sequencing approaches in examining the X, are advancing a dedicated study of this sex chromosome and leading to potentially meaningful discoveries.

Analysis of over one million individuals revealed four loci with genome-wide significance, with a lead variant within SLC9A7 , a transporter molecule that contributes to pH homeostasis within the Golgi apparatus. It will be particularly interesting to know how genetic variation alters cell type-specific SLC9A7 levels and function, and how that links to AD risk—an important mechanistic charge for basic and translational bench research.

It is notable that the two similar studies, while significantly smaller, also report X-chromosome wide signals. Collectively, these studies highlight the high value of the X as a contributor to neural-related functions and as a source of sex difference.

While genetic variation of the X chromosome is an important broad-stroke approach to examining this sex chromosome, X biology may contribute to risk and resilience of AD in several ways, including through gene expression and epigenetic alterations. This is particularly important because females harbor two X chromosomes, and while one is epigenetically inactivated compared to males, the “silent X” partially escapes inactivation and therefore increases the “dose” of the X in females.

In up-and-coming advances, we will understand more about how aging and Alzheimer’s modulate the inactive X, and how that influences sex-based risk and resilience. At the end of the day, the new XWASes and other X studies are pivotal because they could pave the way to new therapeutic targets that benefit men, women, or both sexes.

Belloy et al. performed an XWAS for AD by analyzing 15,081 clinical-AD cases, 41,091 registry-AD and Alzheimer’s disease and dementia (ADD) cases and 82,386 proxy-ADD cases. They identified a genome-wide significant (P<5x10-8) signal in the SLC9A7 locus, with a low effect size (OR=1.054 (1.035-1.075)). They additionally identified five X-chromosome-wide significant (defined as P < 1x10-5) signals. This work addresses an important gap in the genetics of AD, as the X chromosome was excluded from the large-scale GWAS on AD.

In the European Alzheimer & Dementia Biobank (EADB), the International Genomics of Alzheimer’s Project (IGAP), and two biobanks, we also performed a large-scale XWAS for AD on 52,214 clinical-AD cases, 7,759 registry-AD cases and 55,868 proxy-ADD cases. Even though we considered two additional models of X-chromosome inactivation compared to the Belloy study, we did not identify any genome-wide significant signals, but did identify seven X-chromosome-wide significant loci, considering a stricter threshold of P ≤ 1.6×10 −6 than did Belloy et al.

However, the loci we and Belloy identified do not overlap. Though we both found signals in the NLGN4X region, they are different: the two index variants (rs150798997 in Belloy et al., rs4364769 in our study) are located 270,925 bp away, and there is no linkage disequilibrium as determined in the EADB-core dataset. It is noteworthy that, even if we do not replicate the signal seen by Belloy at the SLC9A7 index variant (P=1.36x10 -2 ), we did observe a signal in the locus at another variant, but with a lower absolute effect size than in (P=5.2x10 -5 ).

The lack of overlap between the two studies could be due to several reasons, including:

a) some of the loci are false positives; a higher rate of false positives is expected among signals with X-chromosome-wide significance rather than genome-wide significance;

b) the winner’s curse: signals are expected to be slightly inflated in the first study which identified them;

c) a difference in power;

d) the phenotype definition. The proportion of clinical-AD, registry-AD, registry-ADD and proxy-ADD cases is very different between the two studies. Considering that four proxy cases effectively provide the same power as one diagnosed case, the clinical-AD, registry-AD/ADD, and proxy-ADD cases represent 20 percent, 54 percent, and 27 percent, respectively, of the effective number of cases in Belloy et al study, but 71 percent, 10 percent, and 19 percent in our study. Since a higher proportion of non-AD dementia cases is expected in the registry and proxy-ADD cases, this could lead to different genetic signals. Additionally, the proxy-ADD cases definition also differs in the two studies.

In conclusion, these XWAS did not find common genetic risk factors of large effect for AD on the non-pseudoautosomal region of the X-chromosome but identified signals which warrant further investigations, in particular to delineate their impact on AD versus ADD risk. Also, both studies were based on genotyping data, which leads to technical difficulties—for example lower coverage, in particular of the X-chromosome pseudoautosomal regions, lower call rate or lower imputation quality compared to autosomes. Future analyses of sequencing data will help to address some of those issues, and will allow to study the impact of X-chromosome rare or structural variants on AD risk.

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News Citations

Could Juicing Up Trafficking Abolish ApoE4’s Alzheimer’s Risk? 17 Jul 2021

Paper Citations

Peeters SB, Posynick BJ, Brown CJ . Out of the Silence: Insights into How Genes Escape X-Chromosome Inactivation . Epigenomes . 2023 Nov 23;7(4) PubMed .
Pohlkamp T, Xian X, Wong CH, Durakoglugil MS, Werthmann GC, Saido TC, Evers BM, White CL 3rd, Connor J, Hammer RE, Herz J . NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load . Elife . 2021 Oct 7;10 PubMed .
Piard J, Hu JH, Campeau PM, Rzonca S, Van Esch H, Vincent E, Han M, Rossignol E, Castaneda J, Chelly J, Skinner C, Kalscheuer VM, Wang R, Lemyre E, Kosinska J, Stawinski P, Bal J, Hoffman DA, Schwartz CE, Van Maldergem L, Wang T, Worley PF . FRMPD4 mutations cause X-linked intellectual disability and disrupt dendritic spine morphogenesis . Hum Mol Genet . 2018 Feb 15;27(4):589-600. PubMed .
Basilicata MF, Bruel AL, Semplicio G, Valsecchi CI, Aktaş T, Duffourd Y, Rumpf T, Morton J, Bache I, Szymanski WG, Gilissen C, Vanakker O, Õunap K, Mittler G, van der Burgt I, El Chehadeh S, Cho MT, Pfundt R, Tan TY, Kirchhoff M, Menten B, Vergult S, Lindstrom K, Reis A, Johnson DS, Fryer A, McKay V, DDD Study, Fisher RB, Thauvin-Robinet C, Francis D, Roscioli T, Pajusalu S, Radtke K, Ganesh J, Brunner HG, Wilson M, Faivre L, Kalscheuer VM, Thevenon J, Akhtar A . De novo mutations in MSL3 cause an X-linked syndrome marked by impaired histone H4 lysine 16 acetylation . Nat Genet . 2018 Oct;50(10):1442-1451. Epub 2018 Sep 17 PubMed .
Hayward GC, Caceres D, Copeland EN, Baranowski BJ, Mohammad A, Whitley KC, Fajardo VA, MacPherson RE . Characterization of Alzheimer's disease-like neuropathology in Duchenne's muscular dystrophy using the DBA/2J mdx mouse model . FEBS Open Bio . 2022 Jan;12(1):154-162. Epub 2021 Nov 11 PubMed .
Davis EJ, Solsberg CW, White CC, Miñones-Moyano E, Sirota M, Chibnik L, Bennett DA, De Jager PL, Yokoyama JS, Dubal DB . Sex-Specific Association of the X Chromosome With Cognitive Change and Tau Pathology in Aging and Alzheimer Disease . JAMA Neurol . 2021 Oct 1;78(10):1249-1254. PubMed .
Callaerts-Vegh Z, Ahmed T, Vermaercke B, Marynen P, Balschun D, Froyen G, D'Hooge R . Nxf7 deficiency impairs social exploration and spatio-cognitive abilities as well as hippocampal synaptic plasticity in mice . Front Behav Neurosci . 2015;9:179. Epub 2015 Jul 10 PubMed .
Arenaza-Urquijo EM, Boyle R, Casaletto K, Anstey KJ, Vila-Castelar C, Colverson A, Palpatzis E, Eissman JM, Kheng Siang Ng T, Raghavan S, Akinci M, Vonk JM, Machado LS, Zanwar PP, Shrestha HL, Wagner M, Tamburin S, Sohrabi HR, Loi S, Bartrés-Faz D, Dubal DB, Vemuri P, Okonkwo O, Hohman TJ, Ewers M, Buckley RF, Reserve, Resilience and Protective Factors Professional Interest Area, Sex and Gender Professional Interest area and the ADDRESS! Special Interest Group . Sex and gender differences in cognitive resilience to aging and Alzheimer's disease . Alzheimers Dement . 2024 Aug;20(8):5695-5719. Epub 2024 Jul 5 PubMed .

No Available Further Reading

Primary Papers

Belloy ME, Le Guen Y, Stewart I, Williams K, Herz J, Sherva R, Zhang R, Merritt V, Panizzon MS, Hauger RL, Gaziano JM, Logue M, Napolioni V, Greicius MD . Role of the X Chromosome in Alzheimer Disease Genetics . JAMA Neurol . 2024 Sep 9; PubMed .
LeBorgne J, Gomez L, Heikkinen S, Amin N, Ahmad S, Choi SH, Bis J, Grenier-Boley B, Rodriguez OG, Kleineidam L, Young J, Tripathi KP, Wang L, Varma A, vanderLee S, Damotte V, deRojas I, Palmal S, EADB, GR@ACE, DEGESCO, EADI, GERAD, DemGene, FinnGen, ADGC, CHARGE, Giedraitis V, Ghidoni R, Fernandez V, Kehoe PG, Frikke-Schmidt R, Tsolaki M, Sanchez-Juan P, Sleegers K, Ingelsson M, Haines J, Farrer L, Mayeux R, Wang L-S, Sims R, DeStefano A, Schellenberg GD, Seshadri S, Amouyel P, Wil . X-chromosome-wide association study for Alzheimer's disease . 2024 May 03 10.1101/2024.05.02.24306739 (version 1) medRxiv .
Simmonds E, Leonenko G, Yaman U, Bellou E, Myers A, Morgan K, Brookes K, Hardy J, Salih D, Escott-Price V . Chromosome X-wide association study in case control studies of pathologically confirmed Alzheimer's disease in a European population . Transl Psychiatry . 2024 Sep 4;14(1):358. PubMed .
Buckley RF, Seto M . How Is the X Chromosome Involved in Alzheimer Disease? . JAMA Neurol . 2024 Sep 9; PubMed .

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Current understanding of Alzheimer’s disease diagnosis and treatment

Jason weller.

1 Department of Neurology, Boston VA Hospital, 150 South Huntington Street, Jamaica Plain, MA, 02130, USA

2 Department of Neurology, Boston University School of Medicine, 72 East Concord Street C-309, Boston, MA, USA

Andrew Budson

Alzheimer’s disease is the most common cause of dementia worldwide, with the prevalence continuing to grow in part because of the aging world population. This neurodegenerative disease process is characterized classically by two hallmark pathologies: β-amyloid plaque deposition and neurofibrillary tangles of hyperphosphorylated tau. Diagnosis is based upon clinical presentation fulfilling several criteria as well as fluid and imaging biomarkers. Treatment is currently targeted toward symptomatic therapy, although trials are underway that aim to reduce the production and overall burden of pathology within the brain. Here, we discuss recent advances in our understanding of the clinical evaluation and treatment of Alzheimer’s disease, with updates regarding clinical trials still in progress.

Dementia is a clinical syndrome characterized by progressive decline in two or more cognitive domains, including memory, language, executive and visuospatial function, personality, and behavior, which causes loss of abilities to perform instrumental and/or basic activities of daily living. Alzheimer’s disease (AD) is by far the most common cause of dementia and accounts for up to 80% of all dementia diagnoses 1 . Although the overall death rate in the United States from stroke and cardiovascular disease is decreasing, the proportion of deaths related to AD is going up, increasing by 89% between 2000 and 2014 2 . Direct and indirect costs for healthcare related to AD are estimated at nearly $500 billion annually 3 . The definitive diagnosis of AD requires post-mortem evaluation of brain tissue, though cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers combined with several relatively new clinical criteria can aid diagnosis in living patients 4 . Current treatments available include cholinesterase inhibitors for patients with any stage of AD dementia and memantine for people with moderate-to-severe AD dementia. These medications have been shown to enhance the quality of life for both patient and caregiver when prescribed at the appropriate time during the course of illness; however, they do not change the course of illness or the rate of decline 5 .

Clinical research is advancing toward more definitive treatment of the hallmark pathology in AD with the expectation that these therapies will attenuate the progressive cognitive decline associated with this illness ( Figure 1 ). This review will attempt to summarize the accepted evaluation methods and describe current and future therapies for patients with suspected AD.

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Rate of decline of memory (M) over time (t, months to years). Memory declines slowly in normal aging (1). Alzheimer’s disease is marked by more rapid cognitive decline, often starting earlier in life (2). Current therapies enhance cognition without changing the rate of decline in AD (3). The anticipated effect of novel therapies is reduction in the rate of decline (4).

Building upon the original 1984 diagnostic criteria, the National Institute on Aging–Alzheimer’s Association (NIA–AA) revised the clinical criteria for the diagnosis of mild cognitive impairment (MCI) and the different stages of dementia due to AD in 2011 6 – 8 . The use of supportive biomarker evidence (imaging, serum, and CSF) of AD pathology were included to aid in the delineation of AD from other forms of dementia as well as in the diagnosis of MCI due to AD. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) re-classified delirium, dementia, amnestic and other geriatric cognitive disorders into the more encompassing neurocognitive disorders 9 . This change was made to better discriminate between different neurodegenerative diseases, such as AD, dementia with Lewy bodies, and frontotemporal dementia, as well as to include both major neurocognitive disorder (equivalent to dementia) and mild neurocognitive disorder (equivalent to MCI) 4 . Finally, the newer criteria allow for the use of current and future biomarkers in the diagnosis of degenerative brain disease.

The development of non-invasive diagnostic imaging recently resulted in a test which increases the diagnostic accuracy in AD 10 . After injection of a radiolabeled tracer agent, patients undergo a specialized PET scan that detects the deposition of amyloid-β (Aβ) peptides into plaques in the living brain. In 2012, clinicians were able to accurately diagnose the disease (later autopsy proven) using this method with up to 96% sensitivity and 100% specificity. Over the next year, this same test demonstrated similar results in patients with milder disease 11 . Nearly a decade after researchers at the University of Pittsburgh created the first tracer, the US Food and Drug Administration approved the use of florbetapir for the detection of AD pathology. Now, the list of amyloid-specific PET ligands includes florbetaben and flutemetamol in addition to florbetapir, all of which have a similar profile 12 , 13 . However, the use of amyloid PET imaging in practice is still limited owing to its cost for most patients, as it is not covered by most insurance carriers. Currently, the majority of patients who undergo amyloid PET imaging do so as part of participation in clinical trials.

A more-invasive but less-costly evaluation involves examination of CSF for Aβ42, hyperphosphorylated tau peptide (p-tau), and total tau protein content 14 . This method has slightly less diagnostic accuracy (85–90%), carries the risks and inconveniences involved with a lumbar puncture procedure, and often takes weeks to obtain results because of the dearth of laboratory facilities which perform the fluid analysis. However, a head-to-head comparison showed no difference in diagnostic accuracy between CSF Aβ42:p-tau ratio and amyloid PET imaging biomarkers, suggesting that the best test for individual patients depends upon availability, cost, and patient/provider preference 15 . Less-invasive serum assays designed to detect the quantity of circulating proteins implicated in AD are currently in development and show promise. In 2017, one test discriminated among normal cognition, MCI, and dementia due to AD in a small number of patients with sensitivities and specificities of 84% and 88%, respectively 16 . Another blood test that shows promise is the serum microRNA profile screen that demonstrated validity and reproducibility in smaller trials 17 . With validation by future larger-scale studies, the hope is that a simple blood test may aid in the diagnosis of AD 18 .

Current treatment

At present, only two classes of pharmacologic therapy are available for patients with AD. The cholinesterase inhibitors donepezil, rivastigmine, and galantamine are recommended therapy for patients with mild, moderate, or severe AD dementia as well as Parkinson’s disease dementia 19 . Memantine, which has activity as both a non-competitive N-methyl-D-aspartate receptor antagonist and a dopamine agonist, is approved for use in patients with moderate-to-severe AD (mini-mental state examination [MMSE] <15) who show difficulty with attention and alertness 20 . For patients who choose alternative therapy, the nutraceutical huperzine A has shown benefit in both memory function and activities of daily living 21 . However, while huperzine A is a government-approved medication outside of the US, it is not regulated by the US Food and Drug Administration and may be subject to fluctuations in potency and purity. Vitamin D deficiency was also identified as an independent risk factor for the development of dementia of any cause, and supplementation is recommended for patients in whom deficiency is diagnosed 22 . Although many retrospective, observational studies alluded to the role of inflammation in the development of AD by showing a reduced risk of AD with the use of non-steroidal anti-inflammatory drugs, a more-thorough investigation failed to note any significant difference in cognitive performance in patients who took these medications 23 . In the past decade, omega-3 fatty acid supplements including fish oil have received much attention owing to their cardiovascular benefits. Two recent randomized, controlled, double-blinded studies showed improvement in thinking and memory in patients with MCI who took fish oil supplements, though these studies were limited by small sample size 24 , 25 .

Finally, the management of cardiovascular risk factors contributes to overall brain health in both cerebrovascular disease and neurodegenerative disease 26 . Recent systematic reviews found that people who adhere to the Mediterranean diet (meals consisting of fresh produce, wholegrains, olive oil, legumes, and seafood while limiting dairy and poultry products and avoiding red meat, sweets, and processed foods) have reduced risk of developing cognitive decline and AD 27 , 28 . Regular aerobic exercise, long known to prevent metabolic conditions such as diabetes mellitus and coronary artery disease, also shows preservation of function and reduces caregiver burden in patients with AD 29 . Not only does physical exercise prevent loss of strength and agility as patients age but it also reduces neuropsychiatric symptoms and the increased care requirements associated with these issues. Recreational physical activity increases cognitive function later in life, with benefit noted regardless of age at the initiation of exercise 30 . Less atrophy was observed in the brains of patients with genetic risk factors for AD who exercised regularly compared with those who did not, suggesting that aerobic activity prevents neurodegeneration 31 . Although larger controlled studies are still needed to examine the long-term effects of physical activity in patients with biomarker-proven AD pathology, the inherent systemic benefits and lack of health risks should lead all healthcare providers to recommend regular exercise for their patients, regardless of cognitive function.

Future treatment

Research into future treatments of AD involve targeting of the etiologic pathologies: neurofibrillary tangles (composed of p-tau) and senile plaques (Aβ). However, there remains debate as to which abnormality is the best target to slow or halt neurologic decline as well as how soon treatment should be initiated 32 , 33 . Another approach aims to fortify transcortical networks and enhance inter-neuronal connections in order to enhance cognitive function 34 . From previous studies, we learned that early identification of an at-risk population and subsequent treatment in the pre-clinical stage is the approach most likely to slow or halt the progression of AD 35 . Clinical trials are underway that aim to recruit asymptomatic patients with a genetic predisposition or biomarkers suggestive of higher risk of developing Alzheimer’s dementia, with results expected early in the next decade. The EU/US/Clinical Trials in AD Task Force in 2016 examined many of these trials in an attempt to identify the most effective measures of patient recruitment and retention, infrastructure development, and patient assessment including biomarkers and objective testing for clinical outcomes 35 . Some of the persistent challenges identified include the timeline of recruitment and recruitment failures, difficulty in predicting success based upon prior studies for certain drugs, and the overall costs for such large-scale clinical trials. With a more cooperative effort between researchers, private and public funding, and screening of at-risk populations, a better predictor of successful clinical trials can be created.

Anti-amyloid

According to the amyloid cascade hypothesis, toxic plaques are the earliest manifestation of disease, a statement supported by evidence of Aβ up to 20 years prior to the onset of symptoms 36 . Researchers found in 2013 that this abnormal amyloid plaque induces the phosphorylation of tau protein, which then spreads almost infectiously via microtubule transport to neighboring neurons, leading to neuronal death 37 . One class of medications developed using this evidence is the monoclonal antibodies (passive immunotherapy). This type of treatment involves injection of an antibody that targets abnormal Aβ and facilitates its removal from the brain. Two such monoclonal antibodies were initially developed in 2014 to remove these plaques from the brains of people with AD 38 , 39 . Neither medication improved cognitive scores in patients with mild-to-moderate disease (MMSE 16–26), leading researchers to conclude that these medications may show benefit only when administered in the early stages of MCI and mild dementia. However, a new study regarding the effect of this class of medication in patients with few to no symptoms (MMSE 20–26) but a positive amyloid PET imaging result also failed to show a significant difference in cognitive outcomes between the study group and asymptomatic controls 40 . Studies involving similar drugs in this class are ongoing, with the goal of improving or preserving cognition in patients with MCI due to AD.

Another approach to decreasing Aβ plaque burden in the brain is the inhibition of the enzymes that produce the Aβ peptide from its precursor, amyloid precursor protein (APP). Currently, multiple drugs are in development which target β-site APP cleaving enzyme 1 (BACE1), which is thought to be essential for the production of Aβ peptides 41 . Though previous studies of BACE1 inhibitors failed to yield meaningful results in human subjects, the novel agent verubecestat recently achieved a more than 40-fold reduction in Aβ levels in the brains of rodents and primates, and it has shown a good safety profile in early human trials 42 . Currently, another drug is under investigation for its effect on memory and cognitive function in older patients with positive biomarkers or family history of AD, known as the EARLY study.

Researchers showed in 2014 that combination therapy with a monoclonal antibody and a BACE1 inhibitor significantly reduced the amount of Aβ in amyloid-producing mice 43 . While there are no current trials underway utilizing this approach in humans, many experts believe that combination therapy employing both approaches to eliminate Aβ will ultimately lead to success in AD treatment 44 .

Since p-tau appears to be the downstream pathology and is likely the direct cause of symptoms in AD, drugs to reduce the burden of this protein are also in development 45 . Many different tau vaccines have shown both safety and efficacy in animal models 46 , and, in one recent small study, an anti-tau drug demonstrated a good safety profile and even stimulated a positive immune response in human patients 47 . Several other early phase trials of drugs which target the tau protein are currently underway, though results are yet to be published 48 . Table 1 outlines the treatments and targets currently under investigation.

Target	Drug	Study phase	Expected completion date	Results
β-Amyloid	CAD106	2	May 2024
	CNP520	2	May 2024
	BAN2401	2	November 2018
	LY3002813	2	December 2020
	Crenezumab	3	October 2022
	Aducanumab	3	April 2022
	UB-311	2	December 2018
	Gantenerumab	3	November 2019
	Solanezumab	3	Terminated May 2017	Not effective
	CT1812	2	Completed October 2016	Safe for phase 3
	Thiethylperazine	2	July 2021
	ID1201	2	December 2018
	NPT088	1	February 2019
	Lu AF20513	1	October 2018
	ABvac40	2	February 2021
	Ponezumab	2	Completed June 2011	Not effective
	ACC-001	2	Completed February 2014	Safe for phase 3
	KHK6640	1	Completed December 2017	None yet
	GSK933776	2	Completed	Not effective
	UB-311	1	Completed	Safe for phase 2
	ABvac40	1	Completed July 2015	Safe for phase 2
BACE1	Lanabecestat	2	September 2019
	JNJ-54861911	2	October 2022
	Elenbecestat	3	December 2020
	LY3202626	2	December 2020
	Verubecestat	3	March 2021
	LY450139	3	Completed April 2011	Not effective
P-tau	IONIS-MAPTRx	1, 2	February 2020
	JNJ-63733657	1	February 2019
	RO7105705	2	September 2022
	ABBV-8E12	2	June 2021
	AADvac1	2	June 2019
	BIIB-092	2	September 2020
	BIIB-080	1	February 2020
	TPI-287	1	Completed May 2017
	TRx0237	3	February 2019
	LY3303560	1	June 2019
APP	Posiphen	1
RAGE	Azeliragon	3	Terminated January 2019	Not effective
Retinoid receptor	Acitretin	2	Completed February 2018
	Bexarotene	2	Completed February 2016

Potential treatments currently undergoing clinical investigation. APP, amyloid precursor protein; BACE1, β-site amyloid precursor protein cleaving enzyme 1; p-tau, hyperphosphorylated tau peptide; RAGE, receptor for advanced glycation end products.

*Medications under investigation as combination therapy. Source: www.clinicaltrials.gov .

Neural circuitry

The failure of some targeted therapies toward Aβ in large-scale clinical trials has led to the hypothesis that, although the abnormal protein is implicated at the onset of AD, the progression of clinical symptoms is due to more global neural network dysfunction 49 . Gamma oscillation, a high-frequency brainwave rhythm, is associated with inter-neuronal communication in virtually all brain networks 50 and may help to distinguish between true and false memories 51 . Recently, researchers at the Massachusetts Institute of Technology found that induction of gamma-frequency oscillations led to reduced Aβ deposition and improved cognitive outcomes in an AD mouse model 52 . This was done by using a non-invasive 40 Hz photic stimulator to entrain the desired frequency in the mouse cortex. This method is also currently in early phase trials in humans, utilizing both visual and auditory stimulation.

As recently as 2010, the diagnosis and management of AD relied upon clinical symptom reporting that fit the pattern of memory dysfunction and loss of functional independence in multiple cognitive domains. With the reclassification system devised by the NIA–AA and DSM-5, the spectrum of AD has grown to include pre-clinical disease and MCI, helping to lay the foundation for early identification of at-risk patients. There are now a few widely available diagnostic studies that augment the clinical evaluation for a more accurate diagnosis of AD pathology, including bodily fluids and imaging studies, with good specificity.

However, the treatment options for AD remain supportive and symptomatic without attenuation of the ultimate prognosis. Medications such as cholinesterase inhibitors and memantine improve memory and alertness, respectively, without changing the life expectancy or overall progression of AD dementia. Lifestyle modifications including diet and exercise remain the only interventions with evidence showing lower AD risk and possible prevention of overall cognitive decline, and these interventions are first-line recommendations for all patients regardless of cognitive function. The pathological features associated with AD, Aβ and p-tau, are the current targets for potential treatments; however, early success in comparative studies and smaller clinical trials are thus far not reproducible in larger-scale administrations. Although limited evidence suggests that earlier identification of AD pathology will lead to better and more-definitive treatment, the results of larger-scale interventions are not yet available for review. Given the rising prevalence and mortality of AD coupled with the growing total healthcare costs, there continues to be a sense of urgency in the medical community to develop effective means for the early diagnosis and successful treatment of this progressive neurodegenerative disease.

Abbreviations

Aβ, amyloid β; AD, Alzheimer’s disease; APP, amyloid precursor protein; BACE1, β-site amyloid precursor protein cleaving enzyme 1; CSF, cerebrospinal fluid; DSM-5, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; MCI, mild cognitive impairment; MMSE, mini-mental state examination; NIA–AA, National Institute on Aging–Alzheimer’s Association; p-tau, hyperphosphorylated tau peptide; PET, positron emission tomography

[version 1; referees: 2 approved]

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

Editorial Note on the Review Process

F1000 Faculty Reviews are commissioned from members of the prestigious F1000 Faculty and are edited as a service to readers. In order to make these reviews as comprehensive and accessible as possible, the referees provide input before publication and only the final, revised version is published. The referees who approved the final version are listed with their names and affiliations but without their reports on earlier versions (any comments will already have been addressed in the published version).

The referees who approved this article are:

Hemachandra Reddy , Garrison Institute on Aging, Texas Tech University Health Sciences Center, Lubbock, TX, USA No competing interests were disclosed.
Erik Portelius , Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden No competing interests were disclosed.

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