Risk Factors for Progression of Alzheimer Disease in a Canadian Population: The Canadian Outcomes Study in Dementia (COSID)

Predictors for survival in patients with Alzheimer's disease: a large comprehensive meta-analysis

The prevalence of Alzheimer's disease (AD) is increasing as the population ages, and patients with AD have a poor prognosis. However, knowledge on factors for predicting the survival of AD remains sparse. Here, we aimed to systematically explore predictors of AD survival. We searched the PubMed, Embase and Cochrane databases for relevant literature from inception to December 2022. Cohort and case-control studies were selected, and multivariable adjusted relative risks (RRs) were pooled by random-effects models. A total of 40,784 reports were identified, among which 64 studies involving 297,279 AD patients were included in the meta-analysis after filtering based on predetermined criteria. Four aspects, including demographic features (n = 7), clinical features or comorbidities (n = 13), rating scales (n = 3) and biomarkers (n = 3), were explored and 26 probable prognostic factors were finally investigated for AD survival. We observed that AD patients who had hyperlipidaemia (RR: 0.69) were at a lower risk of death. In contrast, male sex (RR: 1.53), movement disorders (including extrapyramidal signs) (RR: 1.60) and cancer (RR: 2.07) were detrimental to AD patient survival. However, our results did not support the involvement of education, hypertension, APOE genotype, Aβ42 and t-tau in AD survival. Our study comprehensively summarized risk factors affecting survival in patients with AD, provided a better understanding on the role of different factors in the survival of AD from four dimensions, and paved the way for further research.

The effects of amyloid beta aggregation on neuronal transcription

Alzheimer's disease (AD) is a debilitating condition that impairs cognition and episodic memory. AD is well known for its behavioural phenotype however, knowing its cellular pathology, which is primarily based on the presence of amyloid beta (Aβ) in various aggregation states, is crucial for the development of research efforts against the disorder. The most notable of these aggregation states are the oligomeric and fibril forms of Aβ. This paper aims to describe the transcriptomic profile of neuronal cells exposed to these aggregation states in order to better understand the disorder and identify potential therapeutic genetic targets. The primary findings of this paper illustrate the significant effects of Aβ on genes associated with metabolism as well as the dramatically increased effects of oligomeric Aβ relative to fibril Aβ with respect to the overall changes in gene expression. The presented results also support the further examination of the role of GTPases in the deleterious effects of Aβ.

Trends in prevalence of self-reports of Alzheimer’s disease/dementia among non-institutionalized individuals 45+ in Canada, 1994–2014

Background:

Alzheimer’s disease/dementia (AD) prevalence is of concern globally and in Canada owing to the rapidly aging population and increase in life expectancy. This study explored: (1) trends in the overall prevalence of self-reported AD/dementia by sex, age groups, educational levels, and geographic areas in Canada from 1994 to 2014, and (2) assessed what the observed trends mean in the context of the aging Canadian population and the health care system.

Design and methods:

This cross-sectional study used Canadian national survey data. Data for this study were from two Canadian national health surveys (National Population Health Survey and the Canadian Community Health Survey), between 1994 and 2014. After age-sex standardization, trends in the prevalence of self-reported Alzheimer’s disease/dementia were tracked over time. The two cross-sectional surveys used similar diagnostic criteria over the years.

Results:

The overall prevalence of Alzheimer’s disease/dementia increased from 0.14 in 1994 to 0.80 in 2014 representing a 0.66-point increase over the 20 years. Alzheimer’s disease/dementia prevalence increased with age across all years but was more pronounced after age 80. Men 65+ years, those with lower education, and Canadians living in Central Canada had an increased prevalence.

Conclusions:

The overall prevalence of Alzheimer’s disease/dementia in the community increased over time. This study highlights the importance of establishing effective community-based prevention strategies that focus on minimizing risk and optimizing protection as well as health system capacity strengthening and preparation for long-term care including increased demand for neurologists’ services, increased associated disability, psychosocial difficulties, rising costs, and caregiver burden.

Alzheimer’s Disease: Epidemiology and Clinical Progression

Alzheimer’s disease (AD) is prevalent throughout the world and is the leading cause of dementia in older individuals (aged ≥ 65 years). To gain a deeper understanding of the recent literature on the epidemiology of AD and its progression, we conducted a review of the PubMed-indexed literature (2014–2021) in North America, Europe, and Asia. The worldwide toll of AD is evidenced by rising prevalence, incidence, and mortality due to AD—estimates which are low because of underdiagnosis of AD. Mild cognitive impairment (MCI) due to AD can ultimately progress to AD dementia; estimates of AD dementia etiology among patients with MCI range from 40% to 75% depending on the populations studied and whether the MCI diagnosis was made clinically or in combination with biomarkers. The risk of AD dementia increases with progression from normal cognition with no amyloid-beta (Aβ) accumulation to early neurodegeneration and subsequently to MCI. For patients with Aβ accumulation and neurodegeneration, lifetime risk of AD dementia has been estimated to be 41.9% among women and 33.6% among men. Data on progression from preclinical AD to MCI are sparse, but an analysis of progression across the three preclinical National Institute on Aging and Alzheimer’s Association (NIA-AA) stages suggests that NIA-AA stage 3 (subtle cognitive decline with AD biomarker positivity) could be useful in combination with other tools for treatment decision-making. Factors shown to increase risk include lower Mini-Mental State Examination (MMSE) score, higher Alzheimer’s Disease Assessment Scale (ADAS-cog) score, positive APOE4 status, white matter hyperintensities volume, entorhinal cortex atrophy, cerebrospinal fluid (CSF) total tau, CSF neurogranin levels, dependency in instrumental activities of daily living (IADL), and being female. Results suggest that use of biomarkers alongside neurocognitive tests will become an important part of clinical practice as new disease-modifying therapies are introduced.

Modifiable, Non-Modifiable, and Clinical Factors Associated with Progression of Alzheimer's Disease

There is an extensive literature relating to factors associated with the development of Alzheimer's disease (AD), but less is known about factors which may contribute to its progression. This review examined the literature with regard to 15 factors which were suggested by PubMed search to be positively associated with the cognitive and/or neuropathological progression of AD. The factors were grouped as potentially modifiable (vascular risk factors, comorbidities, malnutrition, educational level, inflammation, and oxidative stress), non-modifiable (age at clinical onset, family history of dementia, gender, Apolipoprotein E ɛ4, genetic variants, and altered gene regulation), and clinical (baseline cognitive level, neuropsychiatric symptoms, and extrapyramidal signs). Although conflicting results were found for the majority of factors, a positive association was found in nearly all studies which investigated the relationship of six factors to AD progression: malnutrition, genetic variants, altered gene regulation, baseline cognitive level, neuropsychiatric symptoms, and extrapyramidal signs. Whether these or other factors which have been suggested to be associated with AD progression actually influence the rate of decline of AD patients is unclear. Therapeutic approaches which include addressing of modifiable factors associated with AD progression should be considered.

Computer-Aided Diagnosis System of Alzheimer's Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model

An improved computer-aided diagnosis (CAD) system is proposed for the early diagnosis of Alzheimer’s disease (AD) based on the fusion of anatomical (magnetic resonance imaging (MRI)) and functional (⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET)) multimodal images, and which helps to address the strong ambiguity or the uncertainty produced in brain images. The merit of this fusion is that it provides anatomical information for the accurate detection of pathological areas characterized in functional imaging by physiological abnormalities. First, quantification of brain tissue volumes is proposed based on a fusion scheme in three successive steps: modeling, fusion and decision. (1) Modeling which consists of three sub-steps: the initialization of the centroids of the tissue clusters by applying the Bias corrected Fuzzy C-Means (FCM) clustering algorithm. Then, the optimization of the initial partition is performed by running genetic algorithms. Finally, the creation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) tissue maps by applying the Possibilistic FCM clustering algorithm. (2) Fusion using a possibilistic operator to merge the maps of the MRI and PET images highlighting redundancies and managing ambiguities. (3) Decision offering more representative anatomo-functional fusion images. Second, a support vector data description (SVDD) classifier is used that must reliably distinguish AD from normal aging and automatically detects outliers. The “divide and conquer” strategy is then used, which speeds up the SVDD process and reduces the load and cost of the calculating. The robustness of the tissue quantification process is proven against noise (20% level), partial volume effects and when inhomogeneities of spatial intensity are high. Thus, the superiority of the SVDD classifier over competing conventional systems is also demonstrated with the adoption of the 10-fold cross-validation approach for synthetic datasets (Alzheimer disease neuroimaging (ADNI) and Open Access Series of Imaging Studies (OASIS)) and real images. The percentage of classification in terms of accuracy, sensitivity, specificity and area under ROC curve was 93.65%, 90.08%, 92.75% and 97.3%; 91.46%, 92%, 91.78% and 96.7%; 85.09%, 86.41%, 84.92% and 94.6% in the case of the ADNI, OASIS and real images respectively.

Application of artificial neural network model in diagnosis of Alzheimer's disease

Background

Alzheimer’s disease has become a public health crisis globally due to its increasing incidence. The purpose of this study was to establish an early warning model using artificial neural network (ANN) for early diagnosis of AD and to explore early sensitive markers for AD.

Methods

A population based nested case-control study design was used. 89 new AD cases with good compliance who were willing to provide urine and blood specimen were selected from the cohort of 2482 community-dwelling elderly aged 60 years and over from 2013 to 2016. For each case, two controls living nearby were identified. Biomarkers for AD in urine and blood, neuropsychological functions and epidemiological parameters were included to analyze potential risk factors of AD. Compared with logistic regression, k-Nearest Neighbor (kNN) and support vector machine (SVM) model, back-propagation neural network of three-layer topology structures was applied to develop the early warning model. The performance of all models were measured by sensitivity, specificity, accuracy, positive prognostic value (PPV), negative prognostic value (NPV), the area under curve (AUC), and were validated using bootstrap resampling.

Results

The average age of AD group was about 5 years older than the non-AD controls (P < 0.001). Patients with AD included a significantly larger proportion of subjects with family history of dementia, compared with non-AD group. After adjusting for age and gender, the concentrations of urinary AD7c-NTP and aluminum in blood were significantly higher in AD group than non-AD group (2.01 ± 1.06 vs 1.03 ± 0.43, 1.74 ± 0.62 vs 1.24 ± 0.41 respectively), but the concentration of Selenium in AD group (2.26 ± 0.59) was significantly lower than that in non-AD group (2.61 ± 1.07). All the models were established using 18 variables that were significantly different between AD patients and controls as independent variables. The ANN model outperformed the other classifiers. The AUC for this ANN was 0.897 and the model obtained the accuracy of 92.13%, the sensitivity of 87.28% and the specificity of 94.74% on the average.

Conclusions

Increased risk of AD may be associated with higher age among senior citizens in urban communities. Urinary AD7c-NTP is clinically valuable for the early diagnosis. The established ANN model obtained a high accuracy and diagnostic efficiency, which could be a low-cost practicable tool for the screening and diagnosis of AD for citizens.

Therapeutic efficacy of the Ginkgo special extract EGb761® within the framework of the mitochondrial cascade hypothesis of Alzheimer's disease

OBJECTIVES

The mitochondrial cascade hypothesis of dementia assumes mitochondrial dysfunction as an important common pathomechanism for the whole spectrum of age-associated memory disorders from cognitive symptoms in the elderly over mild cognitive impairment to Alzheimer's dementia. Thus, a drug such as the Ginkgo special extract EGb 761^® which improves mitochondrial function should be able to ameliorate cognitive deficits over the whole aging spectrum.

METHODS

We review the most relevant publications about effects of EGb 761^® on cognition and synaptic deficits in preclinical studies as well as on cognitive deficits in man from aging to dementia.

RESULTS

EGb 761^® improves mitochondrial dysfunction and cognitive impairment over the whole spectrum of age-associated cognitive disorders in relevant animal models and in vitro experiments, and also shows clinical efficacy in improving cognition over the whole range from aging to Alzheimer's or even vascular dementia.

CONCLUSIONS

EGb 761^® shows clinical efficacy in the treatment of cognitive deficits over the whole spectrum of age-associated memory disorders. Thus, EGb 761^® can serve as an important pharmacological argument for the mitochondrial cascade hypothesis of dementia.

Mitochondrial Pharmacology of Dimebon (Latrepirdine) Calls for a New Look at its Possible Therapeutic Potential in Alzheimer's Disease

Dimebon (latrepirdine), an old antihistaminic drug, showed divergent results in two large clinical trials in Alzheimer disease (AD), which according to our review might be related to the specific pharmacological properties of the drug and the different patient populations included in both studies. Out of the many pharmacological effects of Dimebon, improvement of impaired mitochondrial function seeems to be most relevant for the substantial effects on cognition and behaviour reported in one of the studies, as these effects are already present at the low concentrations of dimebon measured in plasma and tissues of patients and experimental animals. Since impaired mitochondrial function seems to be the major driving force for the progression of the clinical symptoms and since most of the clinical benefits of dimebon originate from an effect on the symptomatic deterioration, mitochondrial improvement can also explain the lack of efficacy of this drug in another clinical trial where symptoms of the patiets remained stable for the time of the study. Accordingly, it seems worthwhile to reevaluate the clinical data to proof that clinical response is correlated with high levels of Neuropsychiatric Symptoms as these show a good relationship to the individual speed of symptomatic decline in AD patients related to mitochondrial dysfunction.

The cost-effectiveness implications of suboptimal treatment for different severities of Alzheimer's disease in the UK

OBJECTIVE

This study aims to evaluate the impact of suboptimal treatment, defined in terms of lower population coverage (percentage of total patient population receiving optimal treatment) and delay to treatment on the cost-effectiveness of pharmacological therapies approved for the treatment of different severities of Alzheimer's disease (AD) in the UK.

METHODS

A 5-year Markov model was used to simulate transition to full-time care, as delay and coverage were varied for AD patients with mild-to-moderate and moderate-to-severe dementia. The time-varying predictive equations, resource use, utilities, treatment effects and mortality were derived using published sources.

RESULTS

For the cohort with moderate-to-severe dementia, cost-effectiveness was optimised when delay was minimised and coverage maximised. For mild-to-moderate dementia, results were similar but varied widely depending on the inputted cost of acetylcholinesterase inhibitors.

CONCLUSIONS

The average cost-effectiveness of pharmacological treatments for AD is sensitive to delays to treatment and population coverage. The results of this study can inform future healthcare policy in order to maximise cost-effectiveness of pharmacological therapies for AD. Copyright © 2017 John Wiley & Sons, Ltd.

The clinical significance of plasma clusterin and Aβ in the longitudinal follow-up of patients with Alzheimer's disease

Background

Clusterin and beta-amyloid (Aβ) are involved in the pathogenesis of Alzheimer’s disease (AD). The clinical significance of plasma clusterin and Aβ in AD progression remains controversial.

Methods

We recruited 322 patients with AD and 88 controls between August 2012 and June 2013. All participants were evaluated at baseline with a clinical assessment, Mini-Mental State Examination (MMSE), and Clinical Dementia Rating (CDR) scales. Patients with AD were evaluated annually with the MMSE and Neuropsychiatric Inventory (NPI) scale during the 2-year follow-up period. The levels of plasma clusterin, Aβ1–40, and Aβ1–42 at baseline were analyzed to study the longitudinal changes in the patient scores on the MMSE and NPI during the follow-up period.

Results

Patients in the highest tertile of plasma clusterin levels showed significantly lower MMSE scores than those in the lowest tertile (p = 0.04). After adjustment for multiple covariates using the generalized estimating equation analysis, there was a significant decrease in the MMSE scores over the 2-year follow-up period among AD patients in the highest tertile of plasma clusterin levels compared with those in the lowest tertile (−2.09, 95% confidence interval (CI) = −3.67 to −0.51, p = 0.01). In apolipoprotein E (ApoE)4-positive AD patients, baseline measurements of the ratio of plasma Aβ1–42/Aβ1–40 in the highest tertile predicted an increase in NPI agitation/aggression scores over the 2-year follow-up period (6.06, 95% CI = 1.20–10.62, p = 0.02).

Conclusions

Plasma clusterin could serve as a biomarker for the severity of cognitive decline. Plasma Aβ in ApoE4-positive AD could predict long-term agitation/aggression symptoms.

The Ontario Neurodegenerative Disease Research Initiative (ONDRI)

Because individuals develop dementia as a manifestation of neurodegenerative or neurovascular disorder, there is a need to develop reliable approaches to their identification. We are undertaking an observational study (Ontario Neurodegenerative Disease Research Initiative [ONDRI]) that includes genomics, neuroimaging, and assessments of cognition as well as language, speech, gait, retinal imaging, and eye tracking. Disorders studied include Alzheimer’s disease, amyotrophic lateral sclerosis, frontotemporal dementia, Parkinson’s disease, and vascular cognitive impairment. Data from ONDRI will be collected into the Brain-CODE database to facilitate correlative analysis. ONDRI will provide a repertoire of endophenotyped individuals that will be a unique, publicly available resource.

Walking ability to predict future cognitive decline in old adults: A scoping review

Early identification of individuals at risk for cognitive decline may facilitate the selection of those who benefit most from interventions. Current models predicting cognitive decline include neuropsychological and/or biological markers. Additional markers based on walking ability might improve accuracy and specificity of these models because motor and cognitive functions share neuroanatomical structures and psychological processes. We reviewed the relationship between walking ability at one point of (mid) life and cognitive decline at follow-up. A systematic literature search identified 20 longitudinal studies. The average follow-up time was 4.5 years. Gait speed quantified walking ability in most studies (n=18). Additional gait measures (n=4) were step frequency, variability and step-length. Despite methodological weaknesses, results revealed that gait slowing (0.68-1.1 m/sec) preceded cognitive decline and the presence of dementia syndromes (maximal odds and hazard ratios of 10.4 and 11.1, respectively). The results indicate that measures of walking ability could serve as additional markers to predict cognitive decline. However, gait speed alone might lack specificity. We recommend gait analysis, including dynamic gait parameters, in clinical evaluations of patients with suspected cognitive decline. Future studies should focus on examining the specificity and accuracy of various gait characteristics to predict future cognitive decline.

Depression as a risk factor for Alzheimer's disease: Genes, steroids, cytokines and neurogenesis - What do we need to know?

Depression (MDD) is prodromal to, and a component of, Alzheimer's disease (AD): it may also be a trigger for incipient AD. MDD is not a unitary disorder, so there may be particular subtypes of early life MDD that pose independent high risks for later AD, though the identification of these subtypes is problematical. There may either be a common pathological event underlying both MDD and AD, or MDD may sensitize the brain to a second event ('hit') that precipitates AD. MDD may also accelerate brain ageing, including altered DNA methylation, increased cortisol but decreasing DHEA and thus the risk for AD. So far, genes predicting AD (e.g. APOEε4) are not risk factors for MDD, and those implicated in MDD (e.g. SLC6A4) are not risks for AD, so a common genetic predisposition looks unlikely. There is as yet no strong indication that an epigenetic event occurs during some forms of MDD that predisposes to later AD, though the evidence is limited. Glucocorticoids (GCs) are disturbed in some cases of MDD and in AD. GCs have marked degenerative actions on the hippocampus, a site of early β-amyloid deposition, and rare genetic variants of GC-regulating enzymes (e.g. 11β-HSD) predispose to AD. GCs also inhibit hippocampal neurogenesis and plasticity, and thus episodic memory, a core symptom of AD. Disordered GCs in MDD may inhibit neurogenesis, but the contribution of diminished neurogenesis to the onset or progression of AD is still debated. GCs and cytokines also reduce BDNF, implicated in both MDD and AD and hippocampal neurogenesis, reinforcing the notion that those cases of MDD with disordered GCs may be a risk for AD. Cytokines, including IL1β, IL6 and TNFα, are increased in the blood in some cases of MDD. They also reduce hippocampal neurogenesis, and increased cytokines are a known risk for later AD. Inflammatory changes occur in both MDD and AD (e.g. raised CRP, TNFα). Both cytokines and GCs can have pro-inflammatory actions in the brain. Inflammation (e.g. microglial activation) may be a common link, but this has not been systematically investigated. We lack substantial, rigorous and comprehensive follow-up studies to better identify possible subtypes of MDD that may represent a major predictor for later AD. This would enable specific interventions during critical episodes of these subtypes of MDD that should reduce this substantial risk.