On the path to 2025: understanding the Alzheimer's disease continuum

Relationship between comprehensive geriatric assessment and amyloid PET in older persons with MCI

Background

The association between amyloid deposition and cognitive, behavioral and physical performance in mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) has been poorly investigated, especially in older persons.

Methods

We studied the in vivo correlation between the amyloid deposition at Positron Emission Tomography (amyloid-PET) and the presence of memory loss, reduced executive function, neuropsychiatric symptoms and physical performance in older persons with MCI. Amyloid-PET was performed with 18F-flutemetamol and quantitatively analyzed.

Results

We evaluated 48 subjects, 21 men and 27 women. We performed in each patient a comprehensive geriatric assessment (CGA) including Mini Mental State Examination (MMSE), Clock Drawing Test (CDT), Activity Daily Living (ADL), Instrumental Activity of Daily Living (IADL), Neuropsychiatric inventory (NPI) questionnaire, 15 Geriatric Depression Scale (GDS), Short Physical Performance Battery (SPPB) and Hand Grip strength. Then, each patient underwent amyloid-PET. Mean age of the enrolled subjects was 74.6 ± 7.8 years. All of these subjects showed preserved cognitive function at MMSE > 24, while 29 of 48 subjects (61.0%) had altered CDT.

Mean NPI score was 6.9 ± 5.9. The mean value of SPPB score was 9.0 ± 2.6, while the average muscle strength of the upper extremities measured by hand grip was 25.6 ± 7.7 Kg. CT/MRI images showed cortical atrophic changes in 26 of the 48 examined subjects (54.0%), while cerebrovascular modifications were present in 31 subjects (64.5%). Pathological burden of amyloid deposits was detected in 25 of 48 (52.0%) patients with a mean value of global z-score of 2.8 (subjects defined as MCI due to AD).

After stratifying subjects in subclasses of clinical alterations, more probability of pathological amyloid deposition was found in subjects with impaired CDT and higher NPI score (O.R. = 3.45 [1.01–11.2], p = 0.04), with both impaired CDT and low physical performance (O.R. = 5.80 [1.04–32.2], p = 0.04), with altered CDT and high NPI score (O.R. = 7.98 [1.38–46.0], p = 0.02), and finally in those subjects with altered CDT, high NPI and low physical performance (O.R. = 5.80 [1.05–32.2], p = 0.04).

Conclusion

Our findings support the recent hypothesis that amyloid deposition could be associated with multiple cerebral dysfunction, mainly affecting executive, behavioral and motor abilities.

Risk for Alzheimer's disease: A review of long-term episodic memory encoding and retrieval fMRI studies

Many risk factors have been identified that predict future progression to Alzheimer's disease (AD). However, clear links have yet to be made between these risk factors and how they affect brain functioning in early stages of AD. We conducted a narrative review and a quantitative analysis to better understand the relationship between nine categories of AD risk (i.e., brain pathology, genetics/family history, vascular health, head trauma, cognitive decline, engagement in daily life, late-life depression, sex/gender, and ethnoracial group) and task-evoked fMRI activity during episodic memory in cognitively-normal older adults. Our narrative review revealed widespread regional alterations of both greater and lower brain activity with AD risk. Nevertheless, our quantitative analysis revealed that a subset of studies converged on two patterns: AD risk was associated with (1) greater brain activity in frontal and parietal regions, but (2) reduced brain activity in hippocampal and occipital regions. The brain regions affected depended on the assessed memory stage (encoding or retrieval). Although the results clearly indicate that AD risks impact brain activity, we caution against using fMRI as a diagnostic tool for AD at the current time because the above consistencies were present among much variability, even among the same risk factor.

NODDI in clinical research

Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.

A method for assessing tissue respiration in anatomically defined brain regions

The survival and function of brain cells requires uninterrupted ATP synthesis. Different brain structures subserve distinct neurological functions, and therefore have different energy production/consumption requirements. Typically, mitochondrial function is assessed following their isolation from relatively large amounts of starting tissue, making it difficult to ascertain energy production/failure in small anatomical locations. In order to overcome this limitation, we have developed and optimized a method to measure mitochondrial function in brain tissue biopsy punches excised from anatomically defined brain structures, including white matter tracts. We describe the procedures for maintaining tissue viability prior to performing the biopsy punches, as well as provide guidance for optimizing punch size and the drug doses needed to assess various aspects of mitochondrial respiration. We demonstrate that our method can be used to measure mitochondrial respiration in anatomically defined subfields within the rat hippocampus. Using this method, we present experimental results which show that a mild traumatic brain injury (mTBI, often referred to as concussion) causes differential mitochondrial responses within these hippocampal subfields and the corpus callosum, novel findings that would have been difficult to obtain using traditional mitochondrial isolation methods. Our method is easy to implement and will be of interest to researchers working in the field of brain bioenergetics and brain diseases.

Dysfunction of the Blood-Brain Barrier-A Key Step in Neurodegeneration and Dementia

The vascular endothelium in the brain is an essential part of the blood-brain-barrier (BBB) because of its very tight structure to secure a functional and molecular separation of the brain from the rest of the body and to protect neurons from pathogens and toxins. Impaired transport of metabolites across the BBB due to its increasing dysfunction affects brain health and cognitive functioning, thus providing a starting point of neurodegenerative diseases. The term “cerebral metabolic syndrome” is proposed to highlight the importance of lifestyle factors in neurodegeneration and to describe the impact of increasing BBB dysfunction on neurodegeneration and dementia, especially in elderly patients. If untreated, the cerebral metabolic syndrome may evolve into dementia. Due to the high energy demand of the brain, impaired glucose transport across the BBB via glucose transporters as GLUT1 renders the brain increasingly susceptible to neurodegeneration. Apoptotic processes are further supported by the lack of essential metabolites of the phosphocholine synthesis. In Alzheimer’s disease (AD), inflammatory and infectious processes at the BBB increase the dysfunction and might be pace-making events. At this point, the potentially highly relevant role of the thrombocytic amyloid precursor protein (APP) in endothelial inflammation of the BBB is discussed. Chronic inflammatory processes of the BBB transmitted to an increasing number of brain areas might cause a lasting build-up of spreading, pore-forming β-amyloid fragments explaining the dramatic progression of the disease. In the view of the essential requirement of an early diagnosis to investigate and implement causal therapeutic strategies against dementia, brain imaging methods are of great importance. Therefore, status and opportunities in the field of diagnostic imaging of the living human brain will be portrayed, comprising diverse techniques such as positron emissions tomography (PET) and functional magnetic resonance imaging (fMRI) to uncover the patterns of atrophy, protein deposits, hypometabolism, and molecular as well as functional alterations in AD.

Mesenchymal Stem Cells: A Potential Therapeutic Strategy for Neurodegenerative Diseases

Neurodegenerative disease is a kind of chronic, progressive nervous system disease characterized by neuron degeneration or apoptosis. Current treatments cannot prevent the development of the disease. Possible alternative treatments include cell therapy, especially with the use of mesenchymal stem cells (MSCs). MSCs are pluripotent stem cells with capacities for self-renewal and multidirectional differentiation. MSCs may serve as a reliable source of neural cells for potential cell replacement therapy or regenerative medicine treatment. Here, we summarized the therapeutic mechanisms of MSCs and how they can contribute to the development of treatments for neurodegenerative diseases.

Progressive tau aggregation does not alter functional brain network connectivity in seeded hTau.P301L mice

Progressive accumulation of hyperphosphorylated tau is a hallmark of various neurodegenerative disorders including Alzheimer's disease. However, to date, the functional effects of tau pathology on brain network connectivity remain poorly understood. To directly interrogate the impact of tau pathology on functional brain connectivity, we conducted a longitudinal experiment in which we monitored a fibril-seeded hTau.P301L mouse model using correlative whole-brain microscopy and resting-state functional MRI. Despite a progressive aggravation of tau pathology across the brain, the major resting-state networks appeared unaffected up to 15 weeks after seeding. Targeted analyses also showed that the connectivity of regions with high levels of hyperphosphorylated tau was comparable to that observed in controls. In line with the ostensible retention of connectivity, no behavioural changes were detected between seeded and control hTau.P301L mice as determined by three different paradigms. Our data indicate that seeded tau pathology, with accumulation of tau aggregates throughout different regions of the brain, does not alter functional connectivity or behaviour in this mouse model. Additional correlative functional studies on different mouse models should help determine whether this is a generalizable trait of tauopathies.

The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease

Background

Quantifying changes in the levels of biological and cognitive markers prior to the clinical presentation of Alzheimer’s disease (AD) will provide a template for understanding the underlying aetiology of the clinical syndrome and, concomitantly, for improving early diagnosis, clinical trial recruitment and treatment assessment. This study aims to characterise continuous changes of such markers and determine their rate of change and temporal order throughout the AD continuum.

Methods

The methodology is founded on the development of stochastic models to estimate the expected time to reach different clinical disease states, for different risk groups, and synchronise short-term individual biomarker data onto a disease progression timeline. Twenty-seven markers are considered, including a range of cognitive scores, cerebrospinal (CSF) and plasma fluid proteins, and brain structural and molecular imaging measures. Data from 2014 participants in the Alzheimer’s Disease Neuroimaging Initiative database is utilised.

Results

The model suggests that detectable memory dysfunction could occur up to three decades prior to the onset of dementia due to AD (ADem). This is closely followed by changes in amyloid-β CSF levels and the first cognitive decline, as assessed by sensitive measures. Hippocampal atrophy could be observed as early as the initial amyloid-β accumulation. Brain hypometabolism starts later, about 14 years before onset, along with changes in the levels of total and phosphorylated tau proteins. Loss of functional abilities occurs rapidly around ADem onset. Neurofilament light is the only protein with notable early changes in plasma levels. The rate of change varies, with CSF, memory, amyloid PET and brain structural measures exhibiting the highest rate before the onset of ADem, followed by a decline. The probability of progressing to a more severe clinical state increases almost exponentially with age. In accordance with previous studies, the presence of apolipoprotein E4 alleles and amyloid-β accumulation can be associated with an increased risk of developing the disease, but their influence depends on age and clinical state.

Conclusions

Despite the limited longitudinal data at the individual level and the high variability observed in such data, the study elucidates the link between the long asynchronous pathophysiological processes and the preclinical and clinical stages of AD.

Pathophysiological Mechanisms of Cognitive Impairment and Neurodegeneration by Toxoplasma gondii Infection

Toxoplasma gondii is an obligate intracellular parasite considered one of the most successful pathogens in the world, owing to its ability to produce long-lasting infections and to persist in the central nervous system (CNS) in most warm-blooded animals, including humans. This parasite has a preference to invade neurons and affect the functioning of glial cells. This could lead to neurological and behavioral changes associated with cognitive impairment. Although several studies in humans and animal models have reported controversial results about the relationship between toxoplasmosis and the onset of dementia as a causal factor, two recent meta-analyses have shown a relative association with Alzheimer’s disease (AD). AD is characterized by amyloid-β (Aβ) peptide accumulation, neurofibrillary tangles, and neuroinflammation. Different authors have found that toxoplasmosis may affect Aβ production in brain areas linked with memory functioning, and can induce a central immune response and neurotransmitter imbalance, which in turn, affect the nervous system microenvironment. In contrast, other studies have revealed a reduction of Aβ plaques and hyperphosphorylated tau protein formation in animal models, which might cause some protective effects. The aim of this article is to summarize and review the newest data in regard to different pathophysiological mechanisms of cerebral toxoplasmosis and their relationship with the development of AD and cognitive impairment. All these associations should be investigated further through clinical and experimental studies.

Quantile graphs for EEG-based diagnosis of Alzheimer's disease

Known as a degenerative and progressive dementia, Alzheimer’s disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here—clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index—showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients’ scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.

Diagnostic Biomarkers for Alzheimer's Disease Using Non-Invasive Specimens

Studies in the field of Alzheimer’s disease (AD) have shown the emergence of biomarkers in biologic fluids that hold great promise for the diagnosis of the disease. A diagnosis of AD at a presymptomatic or early stage may be the key for a successful treatment, with clinical trials currently investigating this. It is anticipated that preventative and therapeutic strategies may be stage-dependent, which means that they have a better chance of success at a very early stage—before critical neurons are lost. Several studies have been investigating the use of cerebrospinal fluid (CSF) and blood as clinical samples for the detection of AD with a number of established core markers, such as amyloid beta (Aβ), total tau (T-tau) and phosphorylated tau (P-tau), being at the center of clinical research interest. The use of oral samples—including saliva and buccal mucosal cells—falls under one of the least-investigated areas in AD diagnosis. Such samples have great potential to provide a completely non-invasive alternative to current CSF and blood sampling procedures. The present work is a thorough review of the results and analytical approaches, including proteomics, metabolomics, spectroscopy and microbiome analyses that have been used for the study and detection of AD using salivary samples and buccal cells. With a few exceptions, most of the studies utilizing oral samples were performed in small cohorts, which in combination with the existence of contradictory results render it difficult to come to a definitive conclusion on the value of oral markers. Proteins such as Aβ, T-tau and P-tau, as well as small metabolites, were detected in saliva and have shown some potential as future AD diagnostics. Future large-cohort studies and standardization of sample preparation and (pre-)analytical factors are necessary to determine the use of these non-invasive samples as a diagnostic tool for AD.

Looking for a Treatment for the Early Stage of Alzheimer's Disease: Preclinical Evidence with Co-Ultramicronized Palmitoylethanolamide and Luteolin

BACKGROUND

At the earliest stage of Alzheimer's disease (AD), although patients are still asymptomatic, cerebral alterations have already been triggered. In addition to beta amyloid (Aβ) accumulation, both glial alterations and neuroinflammation have been documented at this stage. Starting treatment at this prodromal AD stage could be a valuable therapeutic strategy. AD requires long-term care; therefore, only compounds with a high safety profile can be used, such as the new formulation containing palmitoylethanolamide and luteolin (co-ultra PEALut) already approved for human use. Therefore, we investigated it in an in vivo pharmacological study that focused on the prodromal stage of AD.

METHODS

We tested the anti-inflammatory and neuroprotective effects of co-ultra PEALut (5 mg/Kg) administered for 14 days in rats that received once, 5 µg Aβ_(1-42) into the hippocampus.

RESULTS

Glial activation and elevated levels of proinflammatory mediators were observed in Aβ-infused rats. Early administration of co-ultra PEALut prevented the Aβ-induced astrogliosis and microgliosis, the upregulation in gene expression of pro-inflammatory cytokines and enzymes, as well as the reduction of mRNA levels BDNF and GDNF. Our findings also highlight an important neuroprotective effect of co-ultra PEALut treatment, which promoted neuronal survival.

CONCLUSIONS

Our results reveal the presence of cellular and molecular modifications in the prodromal stage of AD. Moreover, the data presented here demonstrate the ability of co-ultra PEALut to normalize such Aβ-induced alterations, suggesting it as a valuable therapeutic strategy.

The A4 study: β-amyloid and cognition in 4432 cognitively unimpaired adults

Objective

To clarify the preclinical stage of Alzheimer’s disease by estimating when β‐amyloid accumulation first becomes associated with changes in cognition.

Methods

Here we studied a large group (N = 4432) of cognitively unimpaired individuals who were screened for inclusion in the A4 trial (age 65–85) to assess the effect of subthreshold levels of β‐amyloid on cognition and to identify which cognitive domains first become affected.

Results

β‐amyloid accumulation was linked to significant cognitive dysfunction in cognitively unimpaired participants with subthreshold levels of β‐amyloid in multiple measures of memory (Logical Memory Delayed Recall, P = 0.03; Free and Cued Selective Reminding Test, P < 0.001), the Preclinical Alzheimer’s Cognitive Composite (P = 0.01), and was marginally associated with decreased executive function (Digit Symbol Substitution, P = 0.07). Significantly, decreased cognitive scores were associated with suprathreshold levels of β‐amyloid, across all measures (P < 0.05). The Free and Cued Selective Reminding Test, a list recall memory test, appeared most sensitive to β‐amyloid ‐related decreases in average cognitive scores, outperforming all other cognitive domains, including the narrative recall memory test, Logical Memory.

Interpretation

Clinical trials for cognitively unimpaired β‐amyloid‐positive individuals will include a large number of individuals where mechanisms downstream from β‐amyloid pathology are already activated. These findings have implications for primary and secondary prevention of Alzheimer’s disease.

Cholinesterase Inhibitors May Not Benefit Mild Cognitive Impairment and Mild Alzheimer Disease Dementia

INTRODUCTION

We investigated whether cholinesterase inhibitors (ChEIs) benefit cognitive outcomes in mild cognitive impairment due to Alzheimer disease (MCI-AD) and in mild AD dementia (ADdem).

METHODS

Data from 2242 individuals, clinically diagnosed with MCI-AD [Clinical Dementia Rating (CDR), 0 or 0.5] or with mild ADdem (CDR, 0.5 or 1), were available from the National Alzheimer's Coordinating Center's (NACC) Uniform Data Set (UDS). General linear mixed models were used to examine the annual change in the CDR Sum of Boxes (CDR-SB) and in neuropsychological performance. We compared slopes before and after ChEI initiation among ChEI users, and also compared the change in scores of ChEI users versus nonusers.

RESULTS

Thirty-four percent of 944 MCI-AD and 72% of 1298 ADdem participants were ChEI users. Cognitive decline was greater after ChEI initiation in MCI-AD and ADdem groups (eg, MCI-AD, CDR-SB: 0.03 points/y before initiation; 0.61 points/y after initiation, P<0.0001). Both MCI-AD and ADdem groups had faster decline after ChEI initiation than nonusers (eg, MCI-AD, CDR-SB: 0.61 points/y, ChEI users; 0.24 points/y, nonusers, P<0.0001).

DISCUSSION

This study suggests that ChEI use may not improve the cognitive course in MCI-AD and mild ADdem.

Cathepsin B in neurodegeneration of Alzheimer's disease, traumatic brain injury, and related brain disorders

Investigations of Alzheimer's disease (AD), traumatic brain injury (TBI), and related brain disorders have provided extensive evidence for involvement of cathepsin B, a lysosomal cysteine protease, in mediating the behavioral deficits and neuropathology of these neurodegenerative diseases. This review integrates findings of cathepsin B regulation in clinical biomarker studies, animal model genetic and inhibitor evaluations, structural studies, and lysosomal cell biological mechanisms in AD, TBI, and related brain disorders. The results together indicate the role of cathepsin B in the behavioral deficits and neuropathology of these disorders. Lysosomal leakage occurs in AD and TBI, and related neurodegeneration, which leads to the hypothesis that cathepsin B is redistributed from the lysosome to the cytosol where it initiates cell death and inflammation processes associated with neurodegeneration. These results together implicate cathepsin B as a major contributor to these neuropathological changes and behavioral deficits. These findings support the investigation of cathepsin B as a potential drug target for therapeutic discovery and treatment of AD, TBI, and TBI-related brain disorders.

Racial and Ethnic Disparities in Serious Psychological Distress Among Those With Alzheimer's Disease and Related Dementias

BACKGROUND

Alzheimer's disease and related dementias (ADRD) is a growing public health challenge. Prior research suggests that non-Hispanic whites (whites), non-Hispanic African Americans (African Americans), and Hispanics have differing risks for ADRD.

OBJECTIVE

To examine the existence of serious psychological distress (SPD) among whites, African Americans, and Hispanics; to calculate the predicted probability of ADRD in whites, African Americans, and Hispanics, and to decompose the differences among ADRD populations, quantifying the burden of higher SPD among African Americans and Hispanics, compared to whites.

DATA AND METHOD

The authors use nationally representative data from the Medical Expenditure Panel Survey (2007-2015) to estimate the association between ADRD and race, ethnicity, and SPD. Using Blinder-Oaxaca decomposition analysis, the authors estimate to what extent higher SPD among Hispanics and African Americans was associated with higher ADRD rates compared to whites.

RESULTS

After controlling for individuals' demographic and socioeconomic characteristics and co-existing medical conditions, the presence of SPD was still significantly associated with a higher likelihood of having ADRD. The model predicted significantly higher likelihood of having ADRD among African Americans (7.1%) and Hispanics (5.7%) compared to whites (4.5%). Higher rates of having SPD among African Americans explained 15% of white-black difference and 40% of the white-Hispanic difference in ADRD rates, respectively.

DISCUSSION AND CONCLUSION

Our findings suggest a significant relationship between SPD and ADRD and that the burden of SPD was greater among African Americans and Hispanics with ADRD. Efficient screening using self-reported SPD, compared to simply using diagnoses codes of mental illness, may be more helpful to reduce racial and ethnic disparities in ADRD.

US, EU, and Japanese Regulatory Guidelines for Development of Drugs for Treatment of Alzheimer's Disease: Implications for Global Drug Development

Drug development guidelines from regulatory authorities provide important information to sponsors on requirements for clinical evidence needed to support approval of new drugs. In the field of Alzheimer’s disease (AD), recently published guidelines are available from EU, US, and Japanese regulatory authorities. In this review, these three guidelines are compared and discussed with emphasis on the recommendations provided for demonstration of efficacy in pivotal clinical trials conducted in predementia stages of AD. Similarities and differences are highlighted, and impact for global drug development is discussed in the context of the new International Conference on Harmonization E17 guideline on multiregional clinical trials. The AD field is characterized by significant challenges as, to date, no drug approval precedence exists in predementia AD despite numerous and ambitious efforts to slow the progression of the disease by pharmacologic intervention. Despite these uncertainties regulatory authorities across regions have blazed a trail for proactive multistakeholder collaboration, involvement, and continuous dialogue, setting a positive example on how to foster a supportive environment for development of new and meaningful treatments for patients with AD globally.

Role of Fluid Biomarkers and PET Imaging in Early Diagnosis and its Clinical Implication in the Management of Alzheimer's Disease

Clinical diagnosis of Alzheimer's disease (AD) is based on symptoms; however, the challenge is to diagnose AD at the preclinical stage with the application of biomarkers and initiate early treatment (still not widely available). Currently, cerebrospinal fluid (CSF) amyloid-β 42 (Aβ42) and tau are used in the clinical diagnosis of AD; nevertheless, blood biomarkers (Aβ42 and tau) are less predictive. Amyloid-positron emission tomography (PET) imaging is an advancement in technology that uses approved radioactive diagnostic agents (florbetapir, flutemetamol, or florbetaben) to estimate Aβ neuritic plaque density in adults with cognitive impairment evaluated for AD and other causes of cognitive decline. There is no cure for AD to date-the disease progression cannot be stopped or reversed; approved pharmacological agents (donepezil, galantamine, and rivastigmine; memantine) provide symptomatic treatment. However, the disease-modifying therapies are promising; aducanumab and CAD106 are in phase III trials for the early stages of AD. In conclusion, core CSF biomarkers reflect pathophysiology of AD in the early and late stages; the application of approved radiotracers have potential in amyloid-PET brain imaging to detect early AD.

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning

Introduction:

Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer’s disease (AD) is a critical yet unmet clinical challenge.

Methods:

We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer’s disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated.

Results:

A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R² = 24%) and memory (R² =25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%–75%.

Discussion:

Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.

Neuroanatomical spread of amyloid β and tau in Alzheimer's disease: implications for primary prevention

With recent advances in our understanding of the continuous pathophysiological changes that begin many years prior to symptom onset, it is now apparent that Alzheimer’s disease cannot be adequately described by discrete clinical stages, but should also incorporate the continuum of biological changes that precede and underlie the clinical representation of the disease. By jointly considering longitudinal changes of all available biomarkers and clinical assessments, variation within individuals can be integrated into a single continuous measure of disease progression and used to identify the earliest pathophysiological changes. Disease time, a measure of disease severity, was estimated using a Bayesian latent time joint mixed-effects model applied to an array of imaging, biomarker and neuropsychological data. Trajectories of regional amyloid β and tau PET uptake were estimated as a function of disease time. Regions with early signs of elevated amyloid β uptake were used to form an early, focal composite and compared to a commonly used global composite, in a separate validation sample. Disease time was estimated in 279 participants (183 cognitively unimpaired individuals, 61 mild cognitive impairment and 35 Alzheimer’s disease dementia patients) with available amyloid β and tau PET data. Amyloid β PET uptake levels in the posterior cingulate and precuneus start high and immediately increase with small increases of disease time. Early elevation in tau PET uptake was found in the inferior temporal lobe, amygdala, banks of the superior temporal sulcus, entorhinal cortex, middle temporal lobe, inferior parietal lobe and the fusiform gyrus. In a separate validation sample of 188 cognitively unimpaired individuals, the early, focal amyloid β PET composite showed a 120% increase in the accumulation rate of amyloid β compared to the global composite (P < 0.001), resulting in a 60% increase in the power to detect a treatment effect in a primary prevention trial design. Ordering participants on a continuous disease time scale facilitates the inspection of the earliest signs of amyloid β and tau pathology. To detect early changes in amyloid β pathology, focusing on the earliest sites of amyloid β accumulation results in more powerful and efficient study designs in early Alzheimer’s disease. Targeted composites could be used to re-examine the thresholds for amyloid β-related study inclusion, especially as the field shifts to focus on primary and secondary prevention. Clinical trials of anti-amyloid β treatments may benefit from the use of focal composites when estimating drug effects on amyloid β and tau changes in populations with minimal amyloid β and tau pathology and limited expected short-term accumulation.

Challenges of Integrative Disease Modeling in Alzheimer's Disease

Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.

Down syndrome and Alzheimer's disease: common molecular traits beyond the amyloid precursor protein

Alzheimer’s disease (AD) is the most prevalent type of dementia. Down syndrome (DS) is the leading genetic risk factor for Early-Onset AD, prematurely presenting the classic pathological features of the brain with AD. Augmented gene dosage, including the APP gene, could partially cause this predisposition. Recent works have revealed that alterations in chromosome location due to the extra Chromosome 21, as well as epigenetic modifications, could promote changes in gene expression other than those from Chromosome 21. As a result, similar pathological features and cellular dysfunctions in DS and AD, including impaired autophagy, lysosomal activity, and mitochondrial dysfunction, could be controlled beyond APP overexpression. In this review, we highlight some recent data regarding the origin of the shared features between DS and AD and explore the mechanisms concerning cognitive deficiencies in DS associated with dementia, which could shed some light into the search for new therapeutic targets for AD treatment.

Sleep dysregulation, memory impairment, and CSF biomarkers during different levels of neurocognitive functioning in Alzheimer's disease course

Background

Alzheimer's disease (AD) is frequently accompanied by sleep impairment, which can induce AD-related neurodegeneration. We herein investigated the sleep architecture, cognition, and cerebrospinal fluid (CSF) biomarkers (tau proteins and β-amyloid₄₂) during AD progression from subjective cognitive impairment (SCI) to mild cognitive impairment (MCI) and eventually to AD dementia, and compared the results with cognitively normal (CN) subjects.

Methods

We included patients affected by SCI, MCI, mild AD, and moderate-to-severe AD in our study along with CN subjects as controls. All the subjects underwent nocturnal polysomnography to investigate sleep, neuropsychological testing to evaluate cognition, and lumbar puncture for CSF AD biomarkers assessment.

Results

Sleep (both rapid eye movement (REM) and non-REM sleep) and memory function are both progressively impaired during the course of AD from SCI to mild and subsequently to moderate AD. Further, sleep dysregulation appears earlier than cognitive deterioration, with a reduction of CSF β-amyloid₄₂ level.

Conclusion

Sleep, memory, and CSF AD biomarkers are closely interrelated in AD progression from the earliest asymptomatic and preclinical stages of the disease related in AD since the earliest and preclinical stages of the disease.

Selective serotonin reuptake inhibitors and Alzheimer's disease

Given the failure to develop disease-modifying therapies for Alzheimer’s disease (AD), strategies aiming at preventing or delaying the onset of the disease are being prioritized. While the debate regarding whether depression is an etiological risk factor or a prodrome of AD rages on, a key determining factor may be the timing of depression onset in older adults. There is increasing evidence that untreated early-onset depression is a risk factor and that late-onset depression may be a catalyst of cognitive decline. Data from animal studies have shown a beneficial impact of selective serotonin reuptake inhibitors on pathophysiological biomarkers of AD including amyloid burden, tau deposits and neurogenesis. In humans, studies focusing on subjects with a prior history of depression also showed a delay in the onset of AD in those treated with most selective serotonin reuptake inhibitors. Paroxetine, which has strong anticholinergic properties, was associated with increased mortality and mixed effects on amyloid and tau deposits in mice, as well as increased odds of developing AD in humans. Although most of the data regarding selective serotonin reuptake inhibitors is promising, findings should be interpreted cautiously because of notable methodological heterogeneity between studies. There is thus a need to conduct large scale randomized controlled trials with long follow up periods to clarify the dose-effect relationship of specific serotonergic antidepressants on AD prevention.

Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer's disease

Evidence suggests exposure to particulate matter with aerodynamic diameter <2.5 μm (PM2.5) may increase the risk for Alzheimer's disease and related dementias. Whether PM2.5 alters brain structure and accelerates the preclinical neuropsychological processes remains unknown. Early decline of episodic memory is detectable in preclinical Alzheimer's disease. Therefore, we conducted a longitudinal study to examine whether PM2.5 affects the episodic memory decline, and also explored the potential mediating role of increased neuroanatomic risk of Alzheimer's disease associated with exposure. Participants included older females (n = 998; aged 73-87) enrolled in both the Women's Health Initiative Study of Cognitive Aging and the Women's Health Initiative Memory Study of Magnetic Resonance Imaging, with annual (1999-2010) episodic memory assessment by the California Verbal Learning Test, including measures of immediate free recall/new learning (List A Trials 1-3; List B) and delayed free recall (short- and long-delay), and up to two brain scans (MRI-1: 2005-06; MRI-2: 2009-10). Subjects were assigned Alzheimer's disease pattern similarity scores (a brain-MRI measured neuroanatomical risk for Alzheimer's disease), developed by supervised machine learning and validated with data from the Alzheimer's Disease Neuroimaging Initiative. Based on residential histories and environmental data on air monitoring and simulated atmospheric chemistry, we used a spatiotemporal model to estimate 3-year average PM2.5 exposure preceding MRI-1. In multilevel structural equation models, PM2.5 was associated with greater declines in immediate recall and new learning, but no association was found with decline in delayed-recall or composite scores. For each interquartile increment (2.81 μg/m3) of PM2.5, the annual decline rate was significantly accelerated by 19.3% [95% confidence interval (CI) = 1.9% to 36.2%] for Trials 1-3 and 14.8% (4.4% to 24.9%) for List B performance, adjusting for multiple potential confounders. Long-term PM2.5 exposure was associated with increased Alzheimer's disease pattern similarity scores, which accounted for 22.6% (95% CI: 1% to 68.9%) and 10.7% (95% CI: 1.0% to 30.3%) of the total adverse PM2.5 effects on Trials 1-3 and List B, respectively. The observed associations remained after excluding incident cases of dementia and stroke during the follow-up, or further adjusting for small-vessel ischaemic disease volumes. Our findings illustrate the continuum of PM2.5 neurotoxicity that contributes to early decline of immediate free recall/new learning at the preclinical stage, which is mediated by progressive atrophy of grey matter indicative of increased Alzheimer's disease risk, independent of cerebrovascular damage.

Advanced diffusion magnetic resonance imaging in patients with Alzheimer's and Parkinson's diseases

The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It has recently become possible to determine pathological changes in the brain without autopsy with the advancement of diffusion magnetic resonance imaging techniques. Diffusion magnetic resonance imaging is a robust tool used to evaluate brain microstructural complexity and integrity, axonal order, density, and myelination via the micron-scale displacement of water molecules diffusing in tissues. Diffusion tensor imaging, a type of diffusion magnetic resonance imaging technique is widely utilized in clinical and research settings; however, it has several limitations. To overcome these limitations, cutting-edge diffusion magnetic resonance imaging techniques, such as diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and free water imaging, have been recently proposed and applied to evaluate the pathology of neurodegenerative diseases. This review focused on the main applications, findings, and future directions of advanced diffusion magnetic resonance imaging techniques in patients with Alzheimer’s and Parkinson’s diseases, the first and second most common neurodegenerative diseases, respectively.

Does early cognitive decline require the presence of both tau and amyloid-β?

This scientific commentary refers to ‘Amyloid and tau imaging biomarkers explain cognitive decline from late middle-age’, by Betthauser et al. (doi: 10.1093/brain/awz378).

Aging and aging-associated diseases: a microRNA-based endocrine regulation hypothesis

Although there are numerous hypotheses explaining the nature of aging and associated processes, two concepts are dominant: (i) aging is a result of cell-autonomous processes, such as the accumulation of DNA mutations, aberrant methylations, protein defects, and shortening of telomeres, leading to either inhibition of cellular proliferation and death of non-dividing terminally differentiated cells or tumor development; (ii) aging is a result of a central program that is switched on at a specific stage of organismic development. The microRNA-based endocrine regulation hypothesis combines the two above concepts by proposing central regulation of cell death occurrences via hypothalamus-pituitary gland (PG)-secreted miRNA hormones, the expression and/or secretion of which are regulated by sex hormones. This hypothesis explains such well-known phenomena as inverse comorbidity of either cancer or Alzheimer’s (AD) and other neurodegenerative diseases; higher AD morbidity and lower frequency of many common types of cancer in women vs. men; higher risk of early AD and lower risk of cancer in subjects with Down syndrome; longer life expectancy in women vs. men and much lower sex-dependent differences, if any, in other mammals; increased lifespans due to hypophysectomy or PG hypofunction; and parabiotic effects of blood or plasma transfusions between young and old animals.

Epigallocatechin-3-Gallate (EGCG) Improves Cognitive Deficits Aggravated by an Obesogenic Diet Through Modulation of Unfolded Protein Response in APPswe/PS1dE9 Mice

Epigallocatechin-3-gallate (EGCG), a catechin found in green tea, has been previously investigated for its neuroprotective effects in vitro and in vivo. In the present study, we aimed to evaluate its possible beneficial effects in a well-established preclinical mixed model of familial Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) based on the use of transgenic APPswe/PS1dE9 (APP/PS1) mice fed with a high fat diet (HFD). C57BL/6 wild-type (WT) and APP/PS1 mice were used in this study. APP/PS1 mice were fed with a palmitic acid-enriched HFD (APP/PS1 HFD) containing 45% of fat mainly from hydrogenated coconut oil. Intraperitoneal glucose tolerance tests (IP-GTT) and insulin tolerance tests (IP-ITT) were performed. Western blot analyses were performed to analyse protein expression, and water maze and novel object recognition test were done to evaluate the cognitive process. EGCG treatment improves peripheral parameters such as insulin sensitivity or liver insulin pathway signalling, as well as central memory deficits. It also markedly increased synaptic markers and cAMP response element binding (CREB) phosphorylation rates, as a consequence of a decrease in the unfolded protein response (UPR) activation through the reduction in the activation factor 4 (ATF4) levels and posterior downregulation of protein tyrosine phosphatase 1B (PTP1B). Moreover, EGCG significantly decreased brain amyloid β (Aβ) production and plaque burden by increasing the levels of α-secretase (ADAM10). Also, it led to a reduction in neuroinflammation, as suggested by the decrease in astrocyte reactivity and toll-like receptor 4 (TLR4) levels. Collectively, evidence suggests that chronic EGCG prevents distinct neuropathological AD-related hallmarks. This study also provides novel insights into the metabolic and neurobiological mechanisms of EGCG against cognitive loss through its effects on UPR function, suggesting that this compound may be a promising disease-modifying treatment for neurodegenerative diseases.

Extracellular Vesicles from Mesenchymal Stem Cells Exert Pleiotropic Effects on Amyloid-β, Inflammation, and Regeneration: A Spark of Hope for Alzheimer's Disease from Tiny Structures?

No cure yet exists for devastating Alzheimer's disease (AD), despite many years and humongous efforts to find efficacious pharmacological treatments. So far, neither designing drugs to disaggregate amyloid plaques nor tackling solely inflammation turned out to be decisive. Mesenchymal stem cells (MSCs) and, in particular, extracellular vesicles (EVs) originating from them could be proposed as an alternative, strategic approach to attack the pathology. Indeed, MSC-EVs-owing to their ability to deliver lipids/proteins/enzymes/microRNAs endowed with anti-inflammatory, amyloid-β degrading, and neurotrophic activities-may be exploited as therapeutic tools to restore synaptic function, prevent neuronal death, and slow down memory impairment in AD. Herein the results presented in the most recently published studies on this topic are critically evaluated, providing a strong rationale for possible employment of MSC-EVs in AD. Also see the video abstract here https://youtu.be/tBtDbnlRUhg.

MR Biomarkers of Degenerative Brain Disorders Derived From Diffusion Imaging

The incidence of neurodegenerative diseases has shown an increasing trend. These conditions typically cause progressive functional disability. Identification of robust biomarkers of neurodegenerative diseases is a key imperative to facilitate early identification of the pathological features and to foster a better understanding of the pathogenetic mechanisms of individual diseases. Diffusion tensor imaging (DTI) is the most widely used diffusion MRI technique for assessment of neurodegenerative diseases. The DTI parameters are promising biomarkers for evaluation of microstructural changes; however, some limitations of DTI restrict its wider clinical use. New diffusion MRI techniques, such as diffusion kurtosis imaging (DKI), bi-tensor DTI, and neurite orientation density and dispersion imaging (NODDI) have been demonstrated to provide value addition to DTI for evaluation of neurodegenerative diseases. In this review article, we summarize the key technical aspects and provide an overview of the current state of knowledge regarding the role of DKI, bi-tensor DTI, and NODDI as biomarkers of microstructural changes in representative neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1620-1636.

Reflections on the Utility of the Retina as a Biomarker for Alzheimer's Disease: A Literature Review

As a part of the central nervous system, the retina may reflect both physiologic processes and abnormalities related to diseases of the brain. Indeed, a concerted effort has been put forth to understand how Alzheimer's disease (AD) pathology may manifest in the retina as a means to assess the state of the AD brain. The development and refinement of ophthalmologic techniques for studying the retina in vivo have produced evidence of retinal degeneration in AD diagnosed patients. In this review, we will discuss retinal imaging techniques implemented to study the changes in AD retina as well as highlight the recent efforts made to correlate such findings to other clinical hallmarks of AD to assess the viability of the retina as a biomarker for AD.

Cerebrospinal fluid proteomics and biological heterogeneity in Alzheimer's disease: A literature review

Alzheimer's disease (AD) is the most common cause of dementia and is characterized by aggregation of amyloid and tau proteins in the brain. Results from genetic studies suggest that the pathophysiology underlying AD is complex, but studying this complexity in patients remains difficult. The cerebrospinal fluid (CSF) proteome contains a large number of proteins that can reflect ongoing biological processes. Proteomics techniques can be used to measure many proteins simultaneously in individual patients and may therefore provide an opportunity to study AD disease mechanisms. Here, we review the CSF proteomics literature to identify proteins consistently associated with AD, and perform pathway analyses on these proteins to study which biological processes may be involved in the disease.We performed a literature search of studies that investigated CSF proteomic alterations related to AD. We included original research articles when they measured at least 10 proteins in (antemortem) CSF in at least 10 individuals with AD, mild cognitive impairment (MCI) or controls. We examined if proteins were consistently related to AD, defined as consistent increase or decrease in AD vs. controls across studies. Next, we used the proteins identified as input to pathway analyses using Reactome to investigate which biological processes were enriched.In total, 29 studies were included that investigated AD-related changes to the CSF proteome, including a total of 1434 individuals with AD (of whom 47.1% had a CSF biomarker profile and 9.6% a postmortem examination consistent with AD) and 1380 controls. The studies reported 1 to 138 proteins associated with AD, of which 97 proteins were reported by two or more studies. Among proteins that were measured in more than one study, 27 (27.8%) showed consistent increases, 15 (15.5%) consistent decreases and 55 (56.7%) had contrasting results. Pathway analyses showed that AD-related proteins were enriched for hemostasis, lipoprotein and extracellular matrix pathways.These results indicate that proteomic alterations in CSF associated with AD reflect involvement of various biological pathways. The frequent occurrence of inconsistent protein level changes reported by different studies suggests that additional biological and/or (pre)analytical factors may influence the CSF proteome in AD, which should be further investigated in order to improve understanding of the biological complexity underlying AD.

Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach

Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide and is one of the leading sources of morbidity and mortality in the aging population. There is a long preclinical period followed by mild cognitive impairment (MCI). Clinical diagnosis and the rate of decline is variable. Progression monitoring remains a challenge in AD, and it is imperative to create better tools to quantify this progression. Brain magnetic resonance imaging (MRI) is commonly used for patient assessment. However, current approaches for analysis require strong a priori assumptions about regions of interest used and complex preprocessing pipelines including computationally expensive non-linear registrations and iterative surface deformations. These preprocessing steps are composed of many stacked processing layers. Any error or bias in an upstream layer will be propagated throughout the pipeline. Failures or biases in the non-linear subject registration and the subjective choice of atlases of specific regions are common in medical neuroimaging analysis and may hinder the translation of many approaches to the clinical practice. Here we propose a data-driven method based on an extension of a deep learning architecture, DeepSymNet, that identifies longitudinal changes without relying on prior brain regions of interest, an atlas, or non-linear registration steps. Our approach is trained end-to-end and learns how a patient's brain structure dynamically changes between two-time points directly from the raw voxels. We compare our approach with Freesurfer longitudinal pipelines and voxel-based methods using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model can identify AD progression with comparable results to existing Freesurfer longitudinal pipelines without the need of predefined regions of interest, non-rigid registration algorithms, or iterative surface deformation at a fraction of the processing time. When compared to other voxel-based methods which share some of the same benefits, our model showed a statistically significant performance improvement. Additionally, we show that our model can differentiate between healthy subjects and patients with MCI. The model's decision was investigated using the epsilon layer-wise propagation algorithm. We found that the predictions were driven by the pallidum, putamen, and the superior temporal gyrus. Our novel longitudinal based, deep learning approach has the potential to diagnose patients earlier and enable new computational tools to monitor neurodegeneration in clinical practice.

CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, and is the most common type of cognitive impairment and dementia. There is a pressing need to improve the clinical efficacy and quality of life for AD patients, as limited treatments options for AD patients have been developed until now. In this study, we aim to investigate the protective effect of CH(II), a cerebroprotein hydrolysate consisted of abundant biological peptides, on preclinical model of AD. We found that CH(II) treatment effectively protects oxygen glucose deprivation (OGD)-induced N2A cell viability impairment and cell apoptosis. In addition, CH(II) significantly reduces H2O2-induced ROS accumulation and exhibits the protective activities against H2O2-induced oxidative injury. Intriguingly, we found that CH(II) treatment can effectively promote neurite outgrowth of N2A cells. Moreover, CH(II) obviously improve the cognitive and memorial function in scopolamine-induced amnesia mice model. Taken together, this study provides evidences of the neuroprotective activities of CH(II) and offers a potential therapeutic strategy for AD patients.

Effects of senescence and angiotensin II on expression and processing of amyloid precursor protein in human cerebral microvascular endothelial cells

The present study was designed to determine the effects of senescence and angiotensin II (Ang II) on expression and processing of amyloid precursor protein (APP) in human brain microvascular endothelial cells (BMECs). Senescence caused a decrease in APP expression thereby resulting in reduced secretion of soluble APPα (sAPPα). In contrast, β-site APP cleaving enzyme (BACE1) expression and production of amyloid β (Aβ)40 were increased in senescent endothelium. Importantly, in senescent human BMECs, treatment with BACE1 inhibitor IV inhibited Aβ generation and increased sAPPα production by enhancing a disintegrin and metalloprotease (ADAM)10 expression. Furthermore, Ang II impaired expression of ADAM10 and significantly reduced generation of sAPPα in senescent human BMECs. This inhibitory effect of Ang II was prevented by treatment with BACE1 inhibitor IV. Our results suggest that impairment of α-processing and shift to amyloidogenic pathway of APP contribute to endothelial dysfunction induced by senescence. Loss of sAPPα in senescent cells treated with Ang II exacerbates detrimental effects of senescence on APP processing. Notably, inhibition of BACE1 has beneficial effects on senescence induced endothelial dysfunction. Reported findings may help to explain contributions of senescent cerebral microvascular endothelium to development of cerebral amyloid angiopathy and Alzheimer’s disease (AD) pathology.

Biomarker-guided clustering of Alzheimer's disease clinical syndromes

Alzheimer's disease (AD) neuropathology is extremely heterogeneous, and the evolution from preclinical to mild cognitive impairment until dementia is driven by interacting genetic/biological mechanisms not fully captured by current clinical/research criteria. We characterized the heterogeneous "construct" of AD through a cerebrospinal fluid biomarker-guided stratification approach. We analyzed 5 validated pathophysiological cerebrospinal fluid biomarkers (Aβ_1-42, t-tau, p-tau₁₈₁, NFL, YKL-40) in 113 participants (healthy controls [N = 20], subjective memory complainers [N = 36], mild cognitive impairment [N = 20], and AD dementia [N = 37], age: 66.7 ± 10.4, 70.4 ± 7.7, 71.7 ± 8.4, 76.2 ± 3.5 years [mean ± SD], respectively) using Density-Based Spatial Clustering of Applications with Noise, which does not require a priori determination of the number of clusters. We found 5 distinct clusters (sizes: N = 38, 16, 24, 14, and 21) whose composition was independent of phenotypical groups. Two clusters showed biomarker profiles linked to neurodegenerative processes not associated with classical AD-related pathophysiology. One cluster was characterized by the neuroinflammation biomarker YKL-40. Combining nonlinear data aggregation with informative biomarkers can generate novel patient strata which are representative of cellular/molecular pathophysiology and may aid in predicting disease evolution and mechanistic drug response.

The CSF p-tau181/Aβ42 Ratio Offers a Good Accuracy “In Vivo” in the Differential Diagnosis of Alzheimer’s Dementia

Background:

The incoming disease-modifying therapies against Alzheimer’s disease (AD) require reliable diagnostic markers to correctly enroll patients all over the world. CSF AD biomarkers, namely amyloid-β 42 (Aβ42), total tau (t-tau), and tau phosphorylated at threonine 181 (p-tau181), showed good diagnostic accuracy in detecting AD pathology, but their real usefulness in daily clinical practice is still a matter of debate. Therefore, further validation in complex clinical settings, that is patients with different types of dementia, is needed to uphold their future worldwide adoption.

Methods:

We measured CSF AD biomarkers’ concentrations in a sample of 526 patients with a clinical diagnosis of dementia (277 with AD and 249 with Other Type of Dementia, OTD). Brain FDG-PET was also considered in a subsample of 54 patients with a mismatch between the clinical diagnosis and the CSF findings.

Results:

A p-tau181/Aβ42 ratio higher than 0.13 showed the best diagnostic performance in differentiating AD from OTD (86% accuracy index, 74% sensitivity, 81% specificity). In cases with a mismatch between clinical diagnosis and CSF findings, brain FDG-PET partially agreed with the p-tau181/Aβ42 ratio, thus determining an increase in CSF accuracy.

Conclusions:

The p-tau181/Aβ42 ratio alone might reliably detect AD pathology in heterogeneous samples of patients suffering from different types of dementia. It might constitute a simple, cost-effective and reproducible in vivo proxy of AD suitable to be adopted worldwide not only in daily clinical practice but also in future experimental trials, to avoid the enrolment of misdiagnosed AD patients.

Differentiating Between Healthy Control Participants and Those with Mild Cognitive Impairment Using Volumetric MRI Data

OBJECTIVE

To determine whether volumetric measures of the hippocampus, entorhinal cortex, and other cortical measures can differentiate between cognitively normal individuals and subjects with mild cognitive impairment (MCI).

METHOD

Magnetic resonance imaging (MRI) data from 46 cognitively normal subjects and 50 subjects with MCI as part of the Boston University Alzheimer's Disease Center research registry and the Alzheimer's Disease Neuroimaging Initiative were used in this cross-sectional study. Cortical, subcortical, and hippocampal subfield volumes were generated from each subject's MRI data using FreeSurfer v6.0. Nominal logistic regression models containing these variables were used to identify subjects as control or MCI.

RESULTS

A model containing regions of interest (superior temporal cortex, caudal anterior cingulate, pars opercularis, subiculum, precentral cortex, caudal middle frontal cortex, rostral middle frontal cortex, pars orbitalis, middle temporal cortex, insula, banks of the superior temporal sulcus, parasubiculum, paracentral lobule) fit the data best (R2 = .7310, whole model test chi-square = 97.16, p < .0001).

CONCLUSIONS

MRI data correctly classified most subjects using measures of selected medial temporal lobe structures in combination with those from other cortical areas, yielding an overall classification accuracy of 93.75%. These findings support the notion that, while volumes of medial temporal lobe regions differ between cognitively normal and MCI subjects, differences that can be used to distinguish between these two populations are present elsewhere in the brain.

A short perspective on the long road to effective treatments for Alzheimer's disease

Globally, there are approximately 47 million people living with dementia, and about two thirds of those have Alzheimer's disease (AD). Age is the single biggest risk factor for the vast majority of sporadic AD cases, and because the world's population is aging, the number of people living with AD is set to rise dramatically over the coming decades. There are currently no disease-modifying treatments for AD, and the few symptomatic agents available have limited impact on the disease. Perhaps surprisingly, there is relatively little activity in the AD research and development field compared with other diseases with a high mortality burden, such as cancer. There is enormous economic incentive to discover and develop the first disease-modifying treatment, but previous failure has significantly reduced further industrial investment in this field. The short review looks at the historical path trodden to develop treatments and reflects on the journey down the road to truly effective treatments for people living with AD. LINKED ARTICLES: This article is part of a themed section on Therapeutics for Dementia and Alzheimer's Disease: New Directions for Precision Medicine. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.18/issuetoc.

Association of Head Injury with Brain Amyloid Deposition: The ARIC-PET Study

Our objective was to examine associations of head injury with total and regional brain amyloid deposition. We performed cross-sectional analyses of 329 non-demented participants (81 with prior head injury) in the Atherosclerosis Risk in Communities-Positron Emission Tomography (ARIC-PET) Study who underwent 18-florbetapir PET imaging in 2012-2014. A history of head injury was defined by self-report or emergency department/hospitalization International Classification of Diseases, Ninth Revision codes. Generalized linear regression models adjusted for demographic, socioeconomic, and dementia/cardiovascular risk factors were used to estimate prevalence ratios (PRs; 95% confidence intervals [CIs]) for elevated (> 1.2) global and regional standard uptake value ratios (SUVRs). Mean age of participants was 76 years, 57% were women, and 43% were black. Head injury was associated with increased prevalence of elevated SUVR >1.2 globally (PR: 1.31; 95% CI: 1.19-1.57), as well as in the orbitofrontal cortex (PR: 1.23); (95% CI: 1.04-1.46), prefrontal cortex (PR: 1.18; 95% CI: 1.00-1.39), superior frontal cortex (PR: 1.24; 95% CI: 1.05-1.48), and posterior cingulate (PR: 1.26; 95% CI: 1.04-1.52). There also was evidence for a dose-response relationship, whereby a history of ≥1 head injury was associated with elevated SUVR >1.2 in the prefrontal cortex and superior frontal cortex compared with persons with a history of one head injury (all, p < 0.05). In conclusion, head injury was associated with increased amyloid deposition globally and in the frontal cortex and posterior cingulate, with suggestion of a dose-response association of head injuries with beta-amyloid deposition. Further work is needed to determine if increased amyloid deposition contributes to dementia in this population.

Multivariate Platelet Analysis Differentiates Between Patients with Alzheimer's Disease and Healthy Controls at First Clinical Diagnosis

BACKGROUND

Early diagnosis of Alzheimer's disease (AD) is challenging, and easily accessible biomarkers are an unmet need. Blood platelets frequently serve as peripheral model for studying AD pathogenesis and might represent a reasonable biomarker source.

OBJECTIVE

In the present study, we investigated the potential to differentiate AD patients from healthy controls (HC) based on blood count, platelet morphology, and function as well as molecular markers at the time of first clinical diagnosis.

METHODS

Blood samples from 40 AD patients and 29 age-matched HC were included for determination of 78 parameter by blood counting, platelet morphometry, aggregometry, flow cytometry (CD62P, CD63, activated fibrinogen receptor), protein quantification of nicotinic acetylcholine receptor α7 (nAChRα7) and caveolin-1 (CAV-1), and miRNA quantification (miR-26b, miR-199a, miR-335). Group comparison between patients and controls was performed in univariate and multivariate statistical analyses.

RESULTS

AD patients showed significantly lower aggregation response to ADP and arachidonic acid and significantly decreased CD62P and CD63 surface expression induced by ADP and U46619 compared to HC. Relative nAChRα7 and CAV-1 expression was significantly higher AD platelets than in HC. Multivariate analysis of 63 parameter revealed significant differences between AD patients and healthy controls. The best performing feature model revealed a sensitivity of 96.6%, a specificity of 80.0%, and a positive predictive value of 89.3%. No grouping could be achieved by using single parameter groups.

CONCLUSION

Significant differences between platelet characteristics from AD patients and HC at the time of first clinical diagnosis were observed. The best performing parameter can be used as a blood-based biomarker for AD diagnosis in a multivariate model in addition to the standardized mental tests.

Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease: A Predictive Study

Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g.: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows us to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e., the capacity of predicting biomarker values) is assessed in a cross-validation framework.

The neuropathological diagnosis of Alzheimer's disease

Alzheimer's disease is a progressive neurodegenerative disease most often associated with memory deficits and cognitive decline, although less common clinical presentations are increasingly recognized. The cardinal pathological features of the disease have been known for more than one hundred years, and today the presence of these amyloid plaques and neurofibrillary tangles are still required for a pathological diagnosis. Alzheimer's disease is the most common cause of dementia globally. There remain no effective treatment options for the great majority of patients, and the primary causes of the disease are unknown except in a small number of familial cases driven by genetic mutations. Confounding efforts to develop effective diagnostic tools and disease-modifying therapies is the realization that Alzheimer's disease is a mixed proteinopathy (amyloid and tau) frequently associated with other age-related processes such as cerebrovascular disease and Lewy body disease. Defining the relationships between and interdependence of various co-pathologies remains an active area of investigation. This review outlines etiologically-linked pathologic features of Alzheimer's disease, as well as those that are inevitable findings of uncertain significance, such as granulovacuolar degeneration and Hirano bodies. Other disease processes that are frequent, but not inevitable, are also discussed, including pathologic processes that can clinically mimic Alzheimer's disease. These include cerebrovascular disease, Lewy body disease, TDP-43 proteinopathies and argyrophilic grain disease. The purpose of this review is to provide an overview of Alzheimer's disease pathology, its defining pathologic substrates and the related pathologies that can affect diagnosis and treatment.

Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer's disease with Fortasyn Connect

BACKGROUND

Many prodromal Alzheimer's disease trials collect two types of data: the time until clinical diagnosis of dementia and longitudinal patient information. These data are often analysed separately, although they are strongly associated. By combining the longitudinal and survival data into a single statistical model, joint models can account for the dependencies between the two types of data.

METHODS

We illustrate the major steps in a joint modelling approach, motivated by data from a prodromal Alzheimer's disease study: the LipiDiDiet trial.

RESULTS

By using joint models we are able to disentangle baseline confounding from the intervention effect and moreover, to investigate the association between longitudinal patient information and the time until clinical dementia diagnosis.

CONCLUSIONS

Joint models provide a valuable tool in the statistical analysis of clinical studies with longitudinal and survival data, such as in prodromal Alzheimer's disease trials, and have several added values compared to separate analyses.

Validity of screening instruments for the detection of dementia and mild cognitive impairment in hospital inpatients: A systematic review of diagnostic accuracy studies

INTRODUCTION

As the population ages, Alzheimer's disease and other subtypes of dementia are becoming increasingly prevalent. However, in recent years, diagnosis has often been delayed or not made at all. Thus, improving the rate of diagnosis has become an integral part of national dementia strategies. Although screening for dementia remains controversial, the case is strong for screening for dementia and other forms of cognitive impairment in hospital inpatients. For this reason, the objective of this systematic review was to provide clinicians, who wish to implement screening, an up-to-date choice of cognitive tests with the most extensive evidence base for the use in elective hospital inpatients.

METHODS

For this systematic review, PubMed, PsycINFO and Cochrane Library were searched by using a multi-concept search strategy. The databases were accessed on April 10, 2019. All cross-sectional studies that utilized brief, multi-domain cognitive tests as index test and a reference standard diagnosis of dementia or mild cognitive impairment as comparator were included. Only studies conducted in the hospital setting, sampling from unselected, elective inpatients older than 64 were considered.

RESULTS

Six studies met the inclusion criteria, with a total of 2112 participants. Diagnostic accuracy data for the Six-Item Cognitive Impairment Test, Cognitive Performance Scale, Clock-Drawing Test, Mini-Mental Status Examination, and Time & Change test were extracted and descriptively analyzed. Clinical and methodological heterogeneity between the studies precluded performing a meta-analysis.

DISCUSSION

This review found only a small number of instruments and was not able to recommend a single best instrument for use in a hospital setting. Although it was not possible to estimate the pooled operating characteristics, the included description of instrument characteristics, the descriptive analysis of performance measures, and the critical evaluation of the reporting studies may contribute to clinician's choice of the screening instrument that fits best their purpose.

The Role of Estrogen in Brain and Cognitive Aging

There are 3 common physiological estrogens, of which estradiol (E2) is seen to decline rapidly over the menopausal transition. This decline in E2 has been associated with a number of changes in the brain, including cognitive changes, effects on sleep, and effects on mood. These effects have been demonstrated in both rodent and non-human preclinical models. Furthermore, E2 interactions have been indicated in a number of neuropsychiatric disorders, including Alzheimer's disease, schizophrenia, and depression. In normal brain aging, there are a number of systems that undergo changes and a number of these show interactions with E2, particularly the cholinergic system, the dopaminergic system, and mitochondrial function. E2 treatment has been shown to ameliorate some of the behavioral and morphological changes seen in preclinical models of menopause; however, in clinical populations, the effects of E2 treatment on cognitive changes after menopause are mixed. The future use of sex hormone treatment will likely focus on personalized or precision medicine for the prevention or treatment of cognitive disturbances during aging, with a better understanding of who may benefit from such treatment.