O1–02–01: Forecasting the global prevalence and burden of Alzheimer's disease

Isolation of Phenolic Compounds from Raspberry Based on Molecular Imprinting Techniques and Investigation of Their Anti-Alzheimer's Disease Properties

Alzheimer's disease is the most common neurodegenerative disease, characterized by memory loss and cognitive dysfunction. Raspberry fruits contain polyphenols which have antioxidant and anti-inflammatory properties. In this study, we used molecular imprinting technology to efficiently isolate phenolic components from the raspberry ethyl acetate extracts. Six phenolic components (ellagic acid, tiliroside, kaempferol-3-o-rutoside, gallic acid, ferulic acid and vanillic acid) were identified by UPLC-Q-TOF-MS analysis. Molecular docking was used to predict the anti-inflammatory effects and anti-Alzheimer's potential of these isolated compounds, which showed a good binding ability to diseases and related proteins. However, the binding energy and docking fraction of ellagic acid, tiliroside, and kaempferol-3-o-rutoside were better than those of gallic acid, ferulic acid and vanillic acid. Additionally, by studying the effects of these six phenolic components on the LPS-induced secretion of inflammatory mediators in murine microglial (BV2) cells, it was further demonstrated that they were all capable of inhibiting the secretion of NO, IL-6, TNF-α, and IL-1β to a certain extent. However, ellagic acid, tiliroside, and kaempferol-3-o-rutoside have better inhibitory effects compared to others. The results obtained suggest that the phenolic components extracted from ethyl acetate extracts of raspberry by molecularly imprinted polymers have the potential to inhibit the progression of Alzheimer's disease.

Moderate-Intensity Physical Activity, Music and Art Activities Preserved Cognitive Health in Older Adults: An Argument for Social Prescribing Solution

Introduction: Rates of dementia are projected to increase over the coming years as global populations age. Without a treatment to slow the progression of dementia, many health policies are focusing on preventing dementia by slowing the rate of cognitive decline with age. However, it is unclear which lifestyle changes in old age meaningfully reduce the rate of cognitive decline associated with aging. Objectives: Use existing, multi-year longitudinal health data to determine if engagement in a variety of different lifestyle activities can slow the rate of cognitive decline as older adults age. Method: Data from the English Longitudinal Study of Aging was analyzed using a quasi-experimental, efficient matched-pair design inspired by the clinical trial methodology. Changes in short-term memory scores were assessed over a multi-year interval for groups who undertook one of 11 different lifestyle activities, compared to control groups matched across confounding socioeconomic and lifestyle factors. Results: Two factors, moderate-intensity physical activity and learning activities, resulted in significant positive impact on cognitive function. Conclusion: Our analysis brings cognitive benefit arguments in favor of two lifestyle activities, moderate-intensity physical activity and learning activities, while rejecting other factors advanced by the literature such as vigorous-intensity physical activity. Those findings justify and encourage the development of new lifestyle health programs by health authorities and bring forward the new health system solution, social prescribing.

A Sequential Ensemble Model for Communicable Disease Forecasting

Background:

Ensemble building is a popular method for improving model accuracy for classification problems as well as regression.

Objective:

In this research work, we propose a sequential ensemble model to predict the number of incidences for communicable diseases like influenza, hand foot and mouth disease (HFMD), and diarrhea and compare it with applied models for prediction.

Methods:

The weekly dataset of the three diseases, namely, influenza, HFMD, and diarrhea, are collected from the official government site of Hong Kong from the year 2010 to 2018. The data was preprocessed by taking log transformation and z-score transformation. The proposed sequential ensemble model is applied to the processed dataset to predict future occurrences.

Results:

The result of the proposed ensemble model is compared against standard support vector regression (SVR) using different error metrics such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). In the case of all the threedisease datasets, the proposed ensemble model gives better results in comparison to the standard SVR model.

Conclusion:

The main objective of this research work is to minimize the prediction error; the proposed sequential ensemble model has shown a significant result in terms of prediction errors.

Combining antioxidant astaxantin and cholinesterase inhibitor huperzine A boosts neuroprotection

Oxidative stress is a pathophysiological condition resulting in neurotoxicity, which is possibly associated with neurodegenerative disorders. In this study, the antioxidative effects of the antioxidant astaxanthin (AXT) in combination with huperzine A (HupA), which is used as a cholinesterase inhibitor for the treatment of Alzheimer's disease, were investigated. PC12 cells were treated with either tert-butyl hydroperoxide (TBHP), or with the toxic version of β-amyloid, Aβ_25–35, to induce oxidative stress and neurotoxicity. Cell viability, morphology, lactate dehydrogenase (LDH) release, intracellular accumulation of reactive oxygen species (ROS), superoxide dismutase (SOD) activity and malondialdehyde (MDA) content were determined, while neuroprotection was also monitored using an MTT assay. It was found that combining AXT with HupA significantly increased the viability of PC12 cells, prevented membrane damage (as measured by LDH release), attenuated intracellular ROS formation, increased SOD activity and decreased the level of MDA after TBHP exposure when compared to these drugs administered alone. Pretreatment with HupA and AXT decreased toxic damage produced by Aβ_25–35. These data indicated that combining an antioxidant with a cholinesterase inhibitor increases the degree of neuroprotection; with future investigation this could be a potential therapy used to decrease neurotoxicity in the brain.

Modeling test and treatment strategies for presymptomatic Alzheimer disease

OBJECTIVES

In this study, we developed a model of presymptomatic treatment of Alzheimer disease (AD) after a screening diagnostic evaluation and explored the circumstances required for an AD prevention treatment to produce aggregate net population benefit.

METHODS

Monte Carlo simulation methods were used to estimate outcomes in a simulated population derived from data on AD incidence and mortality. A wide variety of treatment parameters were explored. Net population benefit was estimated in aggregated QALYs. Sensitivity analyses were performed by individually varying the primary parameters.

FINDINGS

In the base-case scenario, treatment effects were uniformly positive, and net benefits increased with increasing age at screening. A highly efficacious treatment (i.e. relative risk 0.6) modeled in the base-case is estimated to save 20 QALYs per 1000 patients screened and 221 QALYs per 1000 patients treated.

CONCLUSIONS

Highly efficacious presymptomatic screen and treat strategies for AD are likely to produce substantial aggregate population benefits that are likely greater than the benefits of aspirin in primary prevention of moderate risk cardiovascular disease (28 QALYS per 1000 patients treated), even in the context of an imperfect treatment delivery environment.

Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease

Imaging biomarkers for Alzheimer's disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer's Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer's disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer's disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer's disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials.

Memory Impairment and Executive Dysfunction are Associated with Inadequately Controlled Diabetes in Older Adults

OBJECTIVE

To assess the cross-sectional relationship of glycemic control to memory impairment and executive dysfunction in older adults with diabetes treated at an urban primary care center.

PARTICIPANTS AND METHODS

As part of a primary care-based cognitive screening program, we identified adults age 65 or older with a diagnosis of diabetes. Glycosylated hemoglobin level (HbA1c) was used to define diabetes as controlled (HbA1c <7) or inadequately controlled(HbA1c ≥ 7). Episodic memory was measured by quartile of free recall scores on the Free and Cued Selective Reminding Test. Executive function was measured using an ordinal composite score derived from animal fluency and months backward. These were the main predictors of diabetic control.

RESULTS

The 169 participants with diabetes had a median age of 74. The sample was 38% African American and 42% Latino. One hundred four (61%) had inadequately controlled diabetes. Memory impairment and executive dysfunction were independent predictors of diabetic control after adjusting for age and education. Binary logistic regression models indicated that the odds of inadequately controlled diabetes was higher for patients in the worst quartile of memory functioning compared to patients in higher quartiles of memory functioning (odds ratio = 6.4; 95% confidence interval: 2.3, 17.6). Any level of executive dysfunction increased the odds of inadequately controlled diabetes compared to patients in the best quintile of executive functioning (odds ratio = 3.6; 95% confidence interval: 1.58, 8.35).

CONCLUSIONS

Memory impairment and executive dysfunction were associated with inadequately controlled diabetes. Though causal inferences are not robust in a cross-sectional study, we suggest that cognitive dysfunction may interfere with diabetes management and that inadequate diabetic control may contribute to cognitive dysfunction.

Identifying early markers of Alzheimer's disease using quantitative multiplex proteomic immunoassay panels

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder with incidence expected to increase four-fold over the next decade. Extensive research efforts are focused upon identifying new treatments, and early diagnosis is considered key to successful intervention. Although imaging and cerebrospinal fluid biomarkers have shown promise in identifying patients in very early stages of the disease, more noninvasive cost-effective tools have remained elusive. Recent studies have reported that an 18-analyte multiplexed plasma panel can differentiate AD from controls suggesting plasma-based screening tools for early AD diagnosis exists. The current study tested the reproducibility of a subset of the original 18-analyte panel using a bead-based multiplex technology. Preliminary results suggest diagnostic accuracy using the subset was 61%. Multivariate analysis of an 89-analyte multivariate panel yielded a diagnostic accuracy of 70% suggesting a plasma-based AD signature that may be a useful screening tool.

Balneotherapy, prevention of cognitive decline and care the Alzheimer patient and his family: outcome of a multidisciplinary workgroup

According to the latest forecasts of the INSEE - Institut National de la Statistique et des Etudes Economiques (National Statistics and Economic Studies Institute), ageing of the French population will increase between 2005 and 2050: whereas 20.8% of the population living in continental France reached the age of 60 years or more in 2005, this proportion would be of 30.6% in 2035 and 31.9% in 2050. In 2050, 22.3 million persons will have reached the age of 60 years or more compared to 12.6 million in 2005, increasing by 80% in a 45-year period. In line with the actual age pyramid, ageing is unavoidable, as those who will reach 60 years of age in 2050 are already born (in 1989 or before). This expansion will be most important between 2006 and 2035, when the numerous "baby-boom" generations born between 1946 and 1975, will reach these ages. In future years, lifespan improvement will only emphasize this increase. Even if life expectancy stabilizes at the 2005 level, the number of seniors reaching 60 years or more would still increase to 50% between 2005 and 2050. This issue is identical in all countries of the European Union. Ageing is a major risk factor for dementia that will considerably worsen in the next years, if no curative therapies are found. Today, 25 million persons in the world suffer from Alzheimer's disease (AD). In France, it is estimated that 860,000 persons are affected and that 225,000 news cases are annually diagnosed. After 75 years of age, more than 20% of women and 13% of men are concerned. Forecasts for the coming years are frightening. Considering ageing of the population, the number of Alzheimer's disease cases should raise to 1.3 million in 2020 (20 patients for 1000 inhabitants) ant 2.1 million in 2040 (30 patients for 1000 inhabitants).

Current and future treatments for Alzheimer's disease

There are currently >5 million people in the United States who have been diagnosed with Alzheimer's disease. That prevalence rate is expected to triple as the population ages. The health and economic burden due to Alzheimer's disease is a worldwide problem, with some of the greatest burden coming from the developing world as people live longer in those societies. Throughout the world, the projected growth of Alzheimer's disease is dramatic. This is a worldwide public health problem of the highest order, and there is a compelling need to develop new treatments and methods of earlier diagnosis need to slow the progression of the disease and lessen its impact.