Prediction models for dementia and neuropathology in the oldest old: the Vantaa 85+ cohort study

Discovery of effective GSK-3β inhibitors as therapeutic potential against Alzheimer's disease: A computational drug design insight

Alzheimer's disease (AD) is mostly thought to be caused by overactivity of glycogen synthase kinase 3-beta (GSK3-β). Therefore, a GSK-3β inhibitor may be a suggested medicine for Alzheimer's therapy. Nowadays, computational techniques are thought to be among the quickest and most affordable options for therapeutic design and drug discovery. Following a preliminary screening of flavonoids for possible protection against cognitive illnesses such as Alzheimer's, Amentoflavone, Curcumin, and Notopterol were shown to be promising candidates. Using molecular docking, the ligand orientation and binding energy in the ATP-binding pocket of GSK-3β were ascertained. Amentoflavone formed a hydrogen bond with the GSK-3β protein's ATP binding site during the molecular docking phase, obtaining the highest negative binding energy. However, when the results moved closer to a molecular dynamics simulation, the findings changed, and Curcumin was shown to be the most potent inhibitor. All structures remained stable during the MD simulation of the GSK-3β protein and its ligands. Moreover, compared to other natural compounds, Curcumin showed higher binding free energy. Therefore, Curcumin may be useful as a polyphenolic flavonoid in the prevention and treatment of AD. Hence, additional research in vitro and in vivo can focus on these flavonoid compounds as an alternative treatment.

Development and internal validation of a risk prediction model for dementia in a rural older population in China

INTRODUCTION

We sought to develop a practical tool for predicting dementia risk among rural-dwelling Chinese older adults.

METHODS

This cohort study included 2220 rural older adults (age ≥ 65 years) who were dementia-free at baseline (2014) and were followed in 2018. Dementia was diagnosed following the DSM-IV criteria. The prediction model was constructed using Cox models. We used C-index and calibration plots to assess model performance, and the decision curve analysis (DCA) to assess clinical usefulness.

RESULTS

During the 4-year follow-up, 134 individuals were diagnosed with dementia. We identified age, education, self-rated AD8 score, marital status, and stroke for the prediction model, with the C-index being 0.79 (95% confidence interval = 0.75-0.83) and the corrected C-index for internal validation being 0.79. Calibration plots showed good performance in predicting up to 4-year dementia risk and DCA indicated good clinical usefulness.

DISCUSSION

The 4-year dementia risk can be accurately predicted using five easily available predictors in a rural Chinese older population.

HIGHLIGHTS

We developed and internally validated a practical tool for dementia risk prediction among a rural older population in China. The prediction tool showed good discrimination and excellent calibration for predicting up to 4-year risk of dementia. The prediction tool can be used to identify individuals at a high risk for dementia for early preventive interventions.

Sociodemographic and clinical profile from the Brazilian very old 90+ study (BRAVO-90+)

Background

Cognitive impairment and disability are frequent among the oldest-old population, particularly in low- and middle-income countries (LMIC), where this population is rapidly increasing. However, studies on people aged 90 or older are scarce in these settings. Here we analyze the characteristics of the Brazilian Very Old 90+ (BRAVO 90+) study, a population-based sample of 90+ older adults who died in Sao Paulo, Brazil.

Objective

To describe clinical and functional characteristics and investigate factors associated with cognitive impairment in Brazilian adults 90 years or older.

Methods

Data were collected at the time of death. Postmortem cognitive evaluation regarding cognitive abilities three months before death was performed using the Clinical Dementia Rating (CDR) scale. We investigated factors associated with cognitive impairment selected by a Lasso regression.

Results

Among 409 participants (mean age = 94 ± 3 years; 72% women; 69% white; average education = 3.3 ± 3.6 years), hypertension, diabetes, and heart failure were prevalent. Most participants had disabilities. The leading causes of death verified by autopsy were pulmonary edema, pneumonia, and ischemic myocardial disease. Although 48% scored a CDR greater or equal to 1, only 51% had a previous dementia diagnosis. Sedentary behavior, osteoarthritis, and depression were associated with higher odds of cognitive impairment, while married status, greater body mass index, hypertension, and neoplasia were related to lower odds.

Conclusions

Cognitive impairment and disability were common among Brazilians aged 90+. The BRAVO 90+ study will provide valuable insights into dementia and resilience in this population.

Evaluation of the Alkaloids as Inhibitors of Human Acetylcholinesterase by Molecular Docking and ADME Prediction

BACKGROUND/AIM

Alzheimer's disease is a complex, incurable to date, multifactorial disease, which suggests the need for continued development of pharmacotherapy.

MATERIALS AND METHODS

A comprehensive literature search was conducted to identify known ligands with anticholinesterase activity, resulting in the discovery of over 100 alkaloids that are also available in the PubChem database. Subsequently, the ligands underwent molecular docking to evaluate their affinity for the target enzyme. The ligands with the greatest affinity were selected for ligand-based virtual screening.

RESULTS

Three potential compounds were identified for further investigation: ZINC000055042508, ZINC000096316348, and ZINC000067 446933. Computational models of absorption, distribution, metabolism, and excretion (ADME) properties prediction using SwissADME suggested that ZINC000055042508 and ZINC000067446933 can permeate the blood-brain barrier and exhibit non-substrate behavior with respect to P-glycoprotein. In contrast, the ProTox-III prediction indicated the potential for all three compounds to penetrate the blood-brain barrier.

CONCLUSION

These alkaloid derivatives warrant further investigation as potential acetylcholinesterase inhibitors for the treatment of Alzheimer's disease.

Neuropathological Heterogeneity of Dementia Due to Combined Pathology in Aged Patients: Clinicopathological Findings in the Vallecas Alzheimer's Reina Sofía Cohort

Background/Objectives: Clinicopathological research in late-life dementia has focused recently on combined neurodegenerative and vascular conditions underlying the high phenotypic heterogeneity of patients. The Vallecas Alzheimer's Reina Sofía (VARS) cohort (n > 550), and particularly the series of associated brain donations (VARSpath cohort) are presented here. The aim of this study is to contribute to research in dementia with a well-characterized cohort from a single center. Methods: A total of 167 patients with complete neuropathological work-ups were analyzed here. The cohort is characterized by a high female predominance (79%), advanced age at death (88 yrs.), and a high frequency of ApoE-e4 haplotype (43%). Results: The main neuropathological diagnosis was Alzheimer's disease (79.6%), followed by vascular dementia (10.2%) and Lewy body dementia (6%). Overall, intermediate-to-high cerebrovascular disease was observed in 38.9%, Lewy body pathology in 57.5%, LATE (TDP-43 pathology) in 70.7%, ARTAG in 53%, and argyrophilic grain disease in 12% of the patients. More than one pathology with a clinically relevant burden of disease was present in 71.1% of the brains, and a selection of premortem neuropsychological and functional scores showed significant correlation with the number of co-pathologies identified in postmortem brains. Conclusions: The VARS cohort, with thorough clinical follow-up, regular blood sampling, 3-Tesla MR, and a high rate of postmortem brain donation, can provide essential multidisciplinary data in the rising age of modifying therapies and biomarkers for Alzheimer's disease and related dementias.

Adiponectin and Inflammatory Marker Levels in the Elderly Patients with Diabetes, Mild Cognitive Impairment and Depressive Symptoms

Some studies suggest that low-grade inflammation and adipokines may be involved in mild cognitive impairment (MCI) and depression in subjects with type 2 diabetes; however, the available data concerning the elderly population are limited. Therefore, we conducted novel research to determine the serum adiponectin, hs-CRP and TNF-α levels in elderly diabetic patients with MCI and depressive symptoms and to identify the factors associated with MCI in this group. A total of 178 diabetic patients (mean age 84.4 ± 3.4 years) were screened for MCI and depressive symptoms. Various biochemical and biomarker data were collected. We found that patients with MCI and depressive symptoms demonstrated lower adiponectin levels and high hs-CRP and TNF-α. In this group, adiponectin concentration was negatively correlated with hs-CRP, TNF-α, HbA1c, and GDS-30 scores and positively correlated with MoCA scores. Multivariable analysis found the risk of MCI to be associated with higher TNF-α levels, fewer years of formal education, an increased number of comorbidities, and the presence of CVD. We concluded that low-grade inflammation and the presence of adipokines are associated with MCI and depressive symptoms in elderly diabetics. Further research should evaluate the suitability of Hs-CRP, TNF-α, and adiponectin as diagnostic markers for MCI and potential therapeutic targets.

Comparison of subjective cognitive decline and polygenic risk score in the prediction of all-cause dementia, Alzheimer's disease and vascular dementia

BACKGROUND

Polygenic risk scores (PRS) and subjective cognitive decline (SCD) are associated with the risk of developing dementia. It remains to examine whether they can improve the established cardiovascular risk factors aging and dementia (CAIDE) model and how their predictive abilities compare.

METHODS

The CAIDE model was applied to a sub-sample of a large, population-based cohort study (n = 5,360; aged 50-75) and evaluated for the outcomes of all-cause dementia, Alzheimer's disease (AD) and vascular dementia (VD) by calculating Akaike's information criterion (AIC) and the area under the curve (AUC). The improvement of the CAIDE model by PRS and SCD was further examined using the net reclassification improvement (NRI) method and integrated discrimination improvement (IDI).

RESULTS

During 17 years of follow-up, 410 participants were diagnosed with dementia, including 139 AD and 152 VD diagnoses. Overall, the CAIDE model showed high discriminative ability for all outcomes, reaching AUCs of 0.785, 0.793, and 0.789 for all-cause dementia, AD, and VD, respectively. Adding information on SCD significantly increased NRI for all-cause dementia (4.4%, p = 0.04) and VD (7.7%, p = 0.01). In contrast, prediction models for AD further improved when PRS was added to the model (NRI, 8.4%, p = 0.03). When APOE ε4 carrier status was included (CAIDE Model 2), AUCs increased, but PRS and SCD did not further improve the prediction.

CONCLUSIONS

Unlike PRS, information on SCD can be assessed more efficiently, and thus, the model including SCD can be more easily transferred to the clinical setting. Nevertheless, the two variables seem negligible if APOE ε4 carrier status is available.

Neuropathologic Burden and Dementia in Nonagenarians and Centenarians: Comparison of 2 Community-Based Cohorts

BACKGROUND AND OBJECTIVES

The aim of this study was to compare 2 large clinicopathologic cohorts of participants aged 90+ and to determine whether the association between neuropathologic burden and dementia in these older groups differs substantially from those seen in younger-old adults.

METHODS

Autopsied participants from The 90+ Study and Adult Changes in Thought (ACT) Study community-based cohort studies were evaluated for dementia-associated neuropathologic changes. Associations between neuropathologic variables and dementia were assessed using logistic or linear regression, and the weighted population attributable fraction (PAF) per type of neuropathologic change was estimated.

RESULTS

The 90+ Study participants (n = 414) were older (mean age at death = 97.7 years) and had higher amyloid/tau burden than ACT <90 (n = 418) (mean age at death = 83.5 years) and ACT 90+ (n = 401) (mean age at death = 94.2 years) participants. The ACT 90+ cohort had significantly higher rates of limbic-predominant age-related TDP-43 encephalopathy (LATE-NC), microvascular brain injury (μVBI), and total neuropathologic burden. Independent associations between individual neuropathologic lesions and odds of dementia were similar between all 3 groups, with the exception of μVBI, which was associated with increased dementia risk in the ACT <90 group only (odds ratio 1.5, 95% CI 1.2-1.8, p < 0.001). Weighted PAF scores indicated that eliminating μVBI, although more prevalent in ACT 90+ participants, would have little effect on dementia. Conversely, eliminating μVBI in ACT <90 could theoretically reduce dementia at a similar rate to that of AD neuropathologic change (weighted PAF = 6.1%, 95% CI 3.8-8.4, p = 0.001). Furthermore, reducing LATE-NC in The 90+ Study could potentially reduce dementia to a greater degree (weighted PAF = 5.1%, 95% CI 3.0-7.3, p = 0.001) than either ACT cohort (weighted PAFs = 1.69, 95% CI 0.4-2.7).

DISCUSSION

Our results suggest that specific neuropathologic features may differ in their effect on dementia among nonagenarians and centenarians from cohorts with different selection criteria and study design. Furthermore, microvascular lesions seem to have a more significant effect on dementia in younger compared with older participants. The results from this study demonstrate that different populations may require distinct dementia interventions, underscoring the need for disease-specific biomarkers.

What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review

Introduction

Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling.

Methods

MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation.

Results

In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups.

Conclusion

The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.

Loss of TDP-43 splicing repression occurs early in the aging population and is associated with Alzheimer's disease neuropathologic changes and cognitive decline

LATE-NC, the neuropathologic changes of limbic-predominant age-related TAR DNA-binding protein 43 kDa (TDP-43) encephalopathy are frequently associated with Alzheimer's disease (AD) and cognitive impairment in older adults. The association of TDP-43 proteinopathy with AD neuropathologic changes (ADNC) and its impact on specific cognitive domains are not fully understood and whether loss of TDP-43 function occurs early in the aging brain remains unknown. Here, using a large set of autopsies from the Baltimore Longitudinal Study of Aging (BLSA) and another younger cohort, we were able to study brains from subjects 21-109 years of age. Examination of these brains show that loss of TDP-43 splicing repression, as judged by TDP-43 nuclear clearance and expression of a cryptic exon in HDGFL2, first occurs during the 6th decade, preceding by a decade the appearance of TDP-43+ neuronal cytoplasmic inclusions (NCIs). We corroborated this observation using a monoclonal antibody to demonstrate a cryptic exon-encoded neoepitope within HDGFL2 in neurons exhibiting nuclear clearance of TDP-43. TDP-43 nuclear clearance is associated with increased burden of tau pathology. Age at death, female sex, high CERAD neuritic plaque score, and high Braak neurofibrillary stage significantly increase the odds of LATE-NC. Faster rates of cognitive decline on verbal memory (California Verbal Learning Test immediate recall), visuospatial ability (Card Rotations Test), mental status (MMSE) and semantic fluency (Category Fluency Test) were associated with LATE-NC. Notably, the effects of LATE-NC on verbal memory and visuospatial ability are independent of ADNC. However, the effects of TDP-43 nuclear clearance in absence of NCI on the longitudinal trajectories and levels of cognitive measures are not significant. These results establish that loss of TDP-43 splicing repression is an early event occurring in the aging population during the development of TDP-43 proteinopathy and is associated with increased tau pathology. Furthermore, LATE-NC correlates with high levels of ADNC but also has an impact on specific memory and visuospatial functions in aging that is independent of AD.

Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study

BACKGROUND

Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia.

OBJECTIVE

This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods.

METHODS

Using Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization.

RESULTS

Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions.

CONCLUSIONS

Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world.

Frequency and correlates of mild cognitive impairment and dementia among the oldest old - Evidence from the representative "Survey on quality of life and subjective well-being of the very old in North Rhine-Westphalia (NRW80+)"

OBJECTIVES

Since there is limited knowledge with regard to the frequency and correlates of mild cognitive impairment and dementia among the oldest old based on large representative data (including institutionalized individuals), our objective was to fill this research gap.

METHODS

For our study, data came from the representative "Survey on quality of life and subjective well-being of the very old in North Rhine-Westphalia (NRW80+)". This study included community-dwelling and institutionalized individuals aged 80 years and over (n = 1,173, mean age: 86 years) residing in the most populous state of Germany (North Rhine-Westphalia). The DemTect was used to quantify cognitive impairment (i.e., probable mild cognitive impairment and probable dementia).

RESULTS

Overall, 73.1% of the individuals were not cognitively impaired, 17.0% of the individuals had probable mild cognitive impairment and 9.9% of the individuals had probable dementia. Compared to individuals without cognitive impairment, individuals with probable mild cognitive impairment were more likely to be male, live in an institutionalized setting, have a lower educational level, have a smaller network size, and have lower functional abilities. Moreover, compared to individuals without cognitive impairment, individuals with probable dementia were more likely to be older, live in an institutionalized setting, have a lower educational level, have a smaller network size, not be multimorbid, and have lower functional abilities.

CONCLUSIONS

Our study identified several sociodemographic and health-related factors which are associated with probable mild cognitive impairment and probable dementia among the oldest old. This knowledge may help to address individuals at risk for mild cognitive impairment and dementia.

External validation of existing dementia prediction models on observational health data

BACKGROUND

Many dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article. In this study, we aim to externally validate existing dementia prediction models. To that end, we define model reporting criteria, review published studies, and externally validate three well reported models using routinely collected health data from administrative claims and electronic health records.

METHODS

We identified dementia prediction models that were developed between 2011 and 2020 and assessed if they could be externally validated given a set of model criteria. In addition, we externally validated three of these models (Walters' Dementia Risk Score, Mehta's RxDx-Dementia Risk Index, and Nori's ADRD dementia prediction model) on a network of six observational health databases from the United States, United Kingdom, Germany and the Netherlands, including the original development databases of the models.

RESULTS

We reviewed 59 dementia prediction models. All models reported the prediction method, development database, and target and outcome definitions. Less frequently reported by these 59 prediction models were predictor definitions (52 models) including the time window in which a predictor is assessed (21 models), predictor coefficients (20 models), and the time-at-risk (42 models). The validation of the model by Walters (development c-statistic: 0.84) showed moderate transportability (0.67-0.76 c-statistic). The Mehta model (development c-statistic: 0.81) transported well to some of the external databases (0.69-0.79 c-statistic). The Nori model (development AUROC: 0.69) transported well (0.62-0.68 AUROC) but performed modestly overall. Recalibration showed improvements for the Walters and Nori models, while recalibration could not be assessed for the Mehta model due to unreported baseline hazard.

CONCLUSION

We observed that reporting is mostly insufficient to fully externally validate published dementia prediction models, and therefore, it is uncertain how well these models would work in other clinical settings. We emphasize the importance of following established guidelines for reporting clinical prediction models. We recommend that reporting should be more explicit and have external validation in mind if the model is meant to be applied in different settings.

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients' cognitive function and early intervention is helpful to improve patient's quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer's disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM. Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM). Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index. Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers. Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.

Preclinical Alzheimer's dementia: a useful concept or another dead end?

The term, preclinical dementia, was introduced in 2011 when new guidelines for the diagnosis of Alzheimer's dementia (AD) were published. In the intervening 11 years, many studies have appeared in the literature focusing on this early stage. A search conducted in English on Google Scholar on 06.23.2022 using the term "preclinical (Alzheimer's) dementia" produced 121, 000 results. However, the label is arguably more relevant for research purposes, and it is possible that the knowledge gained may lead to a cure for AD. The term has not been widely adopted by clinical practitioners. Furthermore, it is still not possible to predict who, after a diagnosis of preclinical dementia, will go on to develop AD, and if so, what the risk factors (modifiable and non-modifiable) might be. This Review/Theoretical article will focus on preclinical Alzheimer's dementia (hereafter called preclinical AD). We outline how preclinical AD is currently defined, explain how it is diagnosed and explore why this is problematic at a number of different levels. We also ask the question: Is the concept 'preclinical AD' useful in clinical practice or is it just another dead end in the Holy Grail to find a treatment for AD? Specific recommendations for research and clinical practice are provided.

Mid- and later-life risk factors for predicting neuropathological brain changes associated with Alzheimer's and vascular dementia: The Honolulu Asia Aging Study and the Age, Gene/Environment Susceptibility-Reykjavik Study

INTRODUCTION

Dementia prediction models are necessary to inform the development of dementia risk reduction strategies. Here, we examine the utility of neuropathological-based risk scores to predict clinical dementia.

METHODS

Models were developed for predicting Alzheimer's disease (AD) and non-AD neuropathologies using the Honolulu Asia Aging neuropathological sub-study (HAAS; n = 852). Model accuracy for predicting clinical dementia, over 30 years, was tested in the non-autopsied HAAS sample (n = 2960) and the Age, Gene/Environment Susceptibility-Reykjavik Study (n = 4614).

RESULTS

Different models were identified for predicting neurodegenerative and vascular neuropathology (c-statistic range: 0.62 to 0.72). These typically included age, APOE, and a blood pressure-related measure. The neurofibrillary tangle and micro-vascular lesion models showed good accuracy for predicting clinical vascular dementia.

DISCUSSION

There may be shared risk factors across dementia-related lesions, suggesting common pathways. Strategies targeting these models may reduce risk or postpone clinical symptoms of dementia as well as reduce neuropathological burden associated with AD and vascular lesions.

Repurposing food molecules as a potential BACE1 inhibitor for Alzheimer's disease

Alzheimer's disease (AD) is a severe neurodegenerative disorder of the brain that manifests as dementia, disorientation, difficulty in speech, and progressive cognitive and behavioral impairment. The emerging therapeutic approach to AD management is the inhibition of β-site APP cleaving enzyme-1 (BACE1), known to be one of the two aspartyl proteases that cleave β-amyloid precursor protein (APP). Studies confirmed the association of high BACE1 activity with the proficiency in the formation of β-amyloid-containing neurotic plaques, the characteristics of AD. Only a few FDA-approved BACE1 inhibitors are available in the market, but their adverse off-target effects limit their usage. In this paper, we have used both ligand-based and target-based approaches for drug design. The QSAR study entails creating a multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model using 552 molecules with acceptable statistical performance (R 2 = 0.82, Q 2 loo = 0.81). According to the QSAR study, the activity has a strong link with various atoms such as aromatic carbons and ring Sulfur, acceptor atoms, sp2-hybridized oxygen, etc. Following that, a database of 26,467 food compounds was primarily used for QSAR-based virtual screening accompanied by the application of the Lipinski rule of five; the elimination of duplicates, salts, and metal derivatives resulted in a truncated dataset of 8,453 molecules. The molecular descriptor was calculated and a well-validated 6-parametric version of the QSAR model was used to predict the bioactivity of the 8,453 food compounds. Following this, the food compounds whose predicted activity (pKi) was observed above 7.0 M were further docked into the BACE1 receptor which gave rise to the Identification of 4-(3,4-Dihydroxyphenyl)-2-hydroxy-1H-phenalen-1-one (PubChem I.D: 4468; Food I.D: FDB017657) as a hit molecule (Binding Affinity = -8.9 kcal/mol, pKi = 7.97 nM, Ki = 10.715 M). Furthermore, molecular dynamics simulation for 150 ns and molecular mechanics generalized born and surface area (MMGBSA) study aided in identifying structural motifs involved in interactions with the BACE1 enzyme. Molecular docking and QSAR yielded complementary and congruent results. The validated analyses can be used to improve a drug/lead candidate's inhibitory efficacy against the BACE1. Thus, our approach is expected to widen the field of study of repurposing nutraceuticals into neuroprotective as well as anti-cancer and anti-viral therapeutic interventions.

Development and validation of a risk model for cognitive impairment in the older Chinese inpatients: An analysis based on a 5-year database

Early diagnosis of cognitive impairment is important but difficult. Prediction models may work as an efficient way to identify high risk individuals for this disease. This study aimed to develop a simple and convenient model to identify high-risk individuals of cognitive impairment in the older Chinese inpatients. We enrolled 1300 inpatients aged 60 years or above from the department of geriatrics of the First Affiliated Hospital of Chongqing Medical University during 2013 to 2017. The model for cognitive impairment was established in the developing cohort of 1100 participants and tested in another validating cohort of 200 participants. Logistic regression analyses were used to identify the candidate variables of cognitive impairment. Receiver operating curve was adopted to validate the model. Logistic regression analyses showed that increasing age, diabetes, depression and low educational level were independently associated with cognitive impairment. The model was generated in the following way: P_model = e^y/(1 + e^y), where y = -6.874 + 0.088 * age + 0.317 * diabetes + 0.647 * depression + 0.345 * education level. The value of P_model indicates the probability of cognitive impairment for each patient. The present model proved to be a reliable marker for identifying people at high risk of cognitive impairment (area under curve = 0.790, 95% CI = 0.728-0.852, p < 0.001). It had a high sensitivity (86.2%) but a relatively low specificity (59.4%). It may be helpful to "recognize" those at high risk of cognitive impairment rather than "rule out" those at low risk of this disease.

A Study of Dementia Prediction Models Based on Machine Learning with Survey Data of Community-Dwelling Elderly People in China

BACKGROUND

For community-dwelling elderly individuals without enough clinical data, it is important to develop a method to predict their dementia risk and identify risk factors for the formulation of reasonable public health policies to prevent dementia.

OBJECTIVE

A community elderly survey data was used to establish machine learning prediction models for dementia and analyze the risk factors.

METHODS

In a cluster-sample community survey of 9,387 elderly people in 5 subdistricts of Wuxi City, data on sociodemographics and neuropsychological self-rating scales for depression, anxiety, and cognition evaluation were collected. Machine learning models were developed to predict their dementia risk and identify risk factors.

RESULTS

The random forest model (AUC = 0.686) had slightly better dementia prediction performance than logistic regression model (AUC = 0.677) and neural network model (AUC = 0.664). The sociodemographic data and psychological evaluation revealed that depression (OR = 3.933, 95% CI = 2.995-5.166); anxiety (OR = 2.352, 95% CI = 1.577-3.509); multiple physical diseases (OR = 2.486, 95% CI = 1.882-3.284 for three or above); "disability, poverty or no family member" (OR = 1.859, 95% CI = 1.337-2.585) and "empty nester" (OR = 1.339, 95% CI = 1.125-1.595) in special family status; "no spouse now" (OR = 1.567, 95% CI = 1.118-2.197); age older than 80 years (OR = 1.645, 95% CI = 1.335-2.026); and female (OR = 1.214, 95% CI = 1.048-1.405) were risk factors for suspected dementia, while a higher education level (OR = 0.365, 95% CI = 0.245-0.546 for college or above) was a protective factor.

CONCLUSION

The machine learning models using sociodemographic and psychological evaluation data from community surveys can be used as references for the prevention and control of dementia in large-scale community populations and the formulation of public health policies.

Apolipoprotein E Genotype e2: Neuroprotection and Its Limits

In this review, we comprehensively, qualitatively, and critically synthesized several features of APOE-e2, a known APOE protective variant, including its associations with longevity, cognition, and neuroimaging, and neuropathology, all in humans. If e2’s protective effects—and their limits—could be elucidated, it could offer therapeutic windows for Alzheimer’s disease (AD) prevention or amelioration. Literature examining e2 within the years 1994–2021 were considered for this review. Studies on human subjects were selectively reviewed and were excluded if observation of e2 was not specified. Effects of e2 were compared with e3 and e4, separately and as a combined non-e2 group. Our examination of existing literature indicated that the most robust protective role of e2 is in longevity and AD neuropathologies, but e2’s effect on cognition and other AD imaging markers (brain structure, function, and metabolism) were inconsistent, thus inconclusive. Notably, e2 was associated with greater risk of non-AD proteinopathies and a disadvantageous cerebrovascular profile. We identified multiple methodological shortcomings of the literature on brain function and cognition that could have contributed to inconsistent and potentially misleading findings. We make careful interpretations of existing findings and provide directions for research strategies that could effectively examine the independent and unbiased effect of e2 on AD risk.

AI-Based Predictive Modelling of the Onset and Progression of Dementia

Dementia, the most severe expression of cognitive impairment, is among the main causes of disability in older adults and currently affects over 55 million individuals. Dementia prevention is a global public health priority, and recent studies have shown that dementia risk can be reduced through non-pharmacological interventions targeting different lifestyle areas. The FINnish GERiatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) has shown a positive effect on cognition in older adults at risk of dementia through a 2-year multidomain intervention targeting lifestyle and vascular risk factors. The LETHE project builds on these findings and will provide a digital-enabled FINGER intervention model for delaying or preventing the onset of cognitive decline. An individualised ICT-based multidomain, preventive lifestyle intervention program will be implemented utilising behaviour and intervention data through passive and active data collection. Artificial intelligence and machine learning methods will be used for data-driven risk factor prediction models. An initial model based on large multinational datasets will be validated and integrated into an 18-month trial integrating digital biomarkers to further improve the model. Furthermore, the LETHE project will investigate the concept of federated learning to, on the one hand, protect the privacy of the health and behaviour data and, on the other hand, to provide the opportunity to enhance the data model easily by integrating additional clinical centres.

Derivation and Validation of the Cognitive Impairment Prediction Model in Older Adults: A National Cohort Study

Objective

This prediction model quantifies the risk of cognitive impairment. This aim of this study was to develop and validate a prediction model to calculate the 6-year risk of cognitive impairment.

Methods

Participants from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2008-2014 and 2011-2018 surveys were included for developing the cognitive impairment prediction model. The least absolute shrinkage and selection operator, clinical knowledge, and previous experience were performed to select predictors. The Cox proportional hazard model and Fine-Gray analysis adjusting for death were conducted to construct the model. The discriminative ability was measured using C-statistics. The model was evaluated externally using the temporal validation method via the CLHLS 2002-2008 survey. A nomogram was conducted to enhance the practical use. The population attributable fraction was calculated.

Results

A total of 10,053 older adults were included for model development. During a median of 5.68 years, 1,750 (17.4%) participants experienced cognitive impairment. Eight easy-to-obtain predictors were used to develop the model. The overall proportion of death was 43.3%. The effect of age on cognitive impairment reduced after adjusting the competing risk of death. The Cox and Fine-Gray models showed a similar discriminative ability, with average C-statistics of 0.71 and 0.69 in development and external validation datasets, respectively. The model performed better in younger older adults (65-74 years). The proportion of 6-year cognitive impairment due to modifiable risk factors was 47.7%.

Conclusion

This model could be used to identify older adults aged 65 years and above at high risk of cognitive impairment and initiate timely interventions on modifiable factors to prevent nearly half of dementia.

Modifiable Risk Factors and Change in Cognition of Māori and Non-Māori in Advanced Age: LiLACS NZ

OBJECTIVE

This study investigated whether previously identified modifiable risk factors for dementia were associated with cognitive change in Māori (indigenous people of New Zealand) and non-Māori octogenarians of LiLACS NZ (Life and Living in Advanced Age; a Cohort Study in New Zealand), a longitudinal study.

METHOD

Multivariable repeated-measure mixed effect regression models were used to assess the association between modifiable risk factors and sociodemographic variables at baseline, and cognitive change over 6 years, with p values of <.05 regarded as statistically significant.

RESULTS

Modifiable factors associated with cognitive change differed between ethnic groups. Depression was a negative factor in Māori only, secondary education in non-Māori was protective, and obesity predicted better cognition over time for Māori. Diabetes was associated with decreased cognition for both Māori and non-Māori.

CONCLUSION

Our results begin to address gaps in the literature and increase understanding of disparities in dementia risk by ethnicity. These findings have implications for evaluating the type and application of culturally appropriate methods to improve cognition.

Inhibition of GSK_3β by Iridoid Glycosides of Snowberry (Symphoricarpos albus) Effective in the Treatment of Alzheimer's Disease Using Computational Drug Design Methods

The inhibition of glycogen synthase kinase-3β (GSK-3β) activity prevents tau hyperphosphorylation and binds it to the microtubule network. Therefore, a GSK-3β inhibitor may be a recommended drug for Alzheimer's treatment. In silico methods are currently considered as one of the fastest and most cost-effective available alternatives for drug/design discovery in the field of treatment. In this study, computational drug design was conducted to introduce compounds that play an effective role in inhibiting the GSK-3β enzyme by molecular docking and molecular dynamics simulation. The iridoid glycosides of the common snowberry (Symphoricarpos albus), including loganin, secologanin, and loganetin, are compounds that have an effect on improving memory and cognitive impairment and the results of which on Alzheimer's have been studied as well. In this study, in the molecular docking phase, loganin was considered a more potent inhibitor of this protein by establishing a hydrogen bond with the ATP-binding site of GSK-3β protein and the most negative binding energy to secologanin and loganetin. Moreover, by molecular dynamics simulation of these ligands and GSK-3β protein, all structures were found to be stable during the simulation. In addition, the protein structure represented no change and remained stable by binding ligands to GSK-3β protein. Furthermore, loganin and loganetin have higher binding free energy than secologanin; thus, these compounds could effectively bind to the active site of GSK-3β protein. Hence, loganin and loganetin as iridoid glycosides can be effective in Alzheimer's prevention and treatment, and thus, further in vitro and in vivo studies can focus on these iridoid glycosides as an alternative treatment.

SARS-CoV-2 (COVID-19) as a Predictor of Neuroinflammation and Neurodegeneration: Potential Treatment Strategies

The SARS-CoV-2 (COVID-19) pandemic has attracted attention to the challenge of neuroinflammation as an unavoidable component of viral infections. Acute neuroinflammatory responses include activation of resident tissue macrophages in the CNS followed by release of a variety of cytokines and chemokines associated with activation of oxidative stress and delayed neuron damage. This makes the search for treatments with indirect anti-inflammatory properties relevant. From this point of view, attention is focused on further study of the treatment of patients with COVID-19 with dipyridamole (Curantil) which, having antiviral and anti-inflammatory effects, can inhibit acute inflammatory activity and progression of fibrosis, is a drug with potential, especially among patients with early increases in the D-dimer concentration and severe signs of microangiopathy.

A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer's disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.

What have we learned from cognition in the oldest-old

PURPOSE OF REVIEW

People over 90 are the fastest growing segment of the population with the highest rates of dementia. This review highlights recent findings that provide insight to our understanding of dementia and cognition at all ages.

RECENT FINDINGS

Risk factors for Alzheimer's disease (AD) and dementia differ by age, with some factors, like the development of hypertension, actually becoming protective in the oldest-old. At least half of all dementia in this age group is due to non AD pathologies, including microinfarcts, hippocampal sclerosis and TDP-43. The number of pathologic changes found in the brain is related to both risk and severity of dementia, but many people in this age group appear to be 'resilient' to these pathologies. Resilience to Alzheimer pathology, in part, may be related to absence of other pathologies, and imaging and spinal fluid biomarkers for AD have limited utility in this age group.

SUMMARY

Studies of dementia in the oldest-old are important for our understanding and eventual treatment or prevention of dementia at all ages.

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71-0.74, AUC for validation = 0.68-0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.

A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study

BACKGROUND

Identifying cognitive impairment early enough could support timely intervention that may hinder or delay the trajectory of cognitive impairment, thus increasing the chances for successful cognitive aging.

OBJECTIVE

We aimed to build a prediction model based on machine learning for cognitive impairment among Chinese community-dwelling elderly people with normal cognition.

METHODS

A prospective cohort of 6718 older people from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) register, followed between 2008 and 2011, was used to develop and validate the prediction model. Participants were included if they were aged 60 years or above, were community-dwelling elderly people, and had a cognitive Mini-Mental State Examination (MMSE) score ≥18. They were excluded if they were diagnosed with a severe disease (eg, cancer and dementia) or were living in institutions. Cognitive impairment was identified using the Chinese version of the MMSE. Several machine learning algorithms (random forest, XGBoost, naïve Bayes, and logistic regression) were used to assess the 3-year risk of developing cognitive impairment. Optimal cutoffs and adjusted parameters were explored in validation data, and the model was further evaluated in test data. A nomogram was established to vividly present the prediction model.

RESULTS

The mean age of the participants was 80.4 years (SD 10.3 years), and 50.85% (3416/6718) were female. During a 3-year follow-up, 991 (14.8%) participants were identified with cognitive impairment. Among 45 features, the following four features were finally selected to develop the model: age, instrumental activities of daily living, marital status, and baseline cognitive function. The concordance index of the model constructed by logistic regression was 0.814 (95% CI 0.781-0.846). Older people with normal cognitive functioning having a nomogram score of less than 170 were considered to have a low 3-year risk of cognitive impairment, and those with a score of 170 or greater were considered to have a high 3-year risk of cognitive impairment.

CONCLUSIONS

This simple and feasible cognitive impairment prediction model could identify community-dwelling elderly people at the greatest 3-year risk for cognitive impairment, which could help community nurses in the early identification of dementia.

Alzheimer's Disease Neuropathological Comorbidities are Common in the Younger-Old

BACKGROUND

Clinicopathological studies have demonstrated that Alzheimer's disease dementia (ADD) is often accompanied by clinically undetectable comorbid neurodegenerative and cerebrovascular disease that alter the rate of cognitive decline. Aside from causing increased variability in clinical response, it is possible that the major ADD comorbidities may not respond to ADD-specific molecular therapeutics.

OBJECTIVE

As most reports have focused on comorbidity in the oldest-old, its extent in younger age groups that are more likely to be involved in clinical trials is largely unknown; our objective is to provide this information.

METHODS

We conducted a survey of neuropathological comorbidities in sporadic ADD using data from the US National Alzheimer's Coordinating Center. Subject data was restricted to those with dementia and meeting National Institute on Aging-Alzheimer's Association intermediate or high AD Neuropathological Change levels, excluding those with known autosomal dominant AD-related mutations.

RESULTS

Highly prevalent ADD comorbidities are not restricted to the oldest-old but are common even in early-onset ADD. The percentage of cases with ADD as the sole major neuropathological diagnosis is highest in the under-60 group, where "pure" ADD cases are still in the minority at 44%. After this AD as a sole major pathology in ADD declines to roughly 20%in the 70s and beyond. Lewy body disease is the most common comorbidity at younger ages but actually is less common at later ages, while for most others, their prevalence increases with age.

CONCLUSION

Alzheimer's disease neuropathological comorbidities are highly prevalent even in the younger-old.

Riluzole Exhibits No Therapeutic Efficacy on a Transgenic Rat model of Amyotrophic Lateral Sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a neurological disorder clinically characterized by motor system dysfunction, with intraneuronal accumulation of the TAR DNAbinding protein 43 (TDP-43) being a pathological hallmark. Riluzole is a primarily prescribed medicine for ALS patients, while its therapeutical efficacy appears limited. TDP-43 transgenic mice are existing animal models for mechanistic/translational research into ALS.

METHODS

We developed a transgenic rat model of ALS expressing a mutant human TDP-43 transgene (TDP-43M337V) and evaluated the therapeutic effect of Riluzole on this model. Relative to control, rats with TDP-43M337V expression promoted by the neurofilament heavy subunit (NEF) gene or specifically in motor neurons promoted by the choline acetyltransferase (ChAT) gene showed progressive worsening of mobility and grip strength, along with loss of motor neurons, microglial activation, and intraneuronal accumulation of TDP-43 and ubiquitin aggregations in the spinal cord.

RESULTS

Compared to vehicle control, intragastric administration of Riluzole (30 mg/kg/d) did not mitigate the behavioral deficits nor alter the neuropathologies in the transgenics.

CONCLUSION

These findings indicate that transgenic rats recapitulate the basic neurological and neuropathological characteristics of human ALS, while Riluzole treatment can not halt the development of the behavioral and histopathological phenotypes in this new transgenic rodent model of ALS.

Antihypertensive medications ameliorate Alzheimer's disease pathology by slowing its propagation

INTRODUCTION

Mounting evidence supports an association between antihypertensive medication use and reduced risk of Alzheimer's disease (AD). Consensus on possible pathological mechanisms remains elusive.

METHODS

Human brain tissue from a cohort followed to autopsy that included 96 cases of AD (46 medicated for hypertension) and 53 pathological controls (33 also medicated) matched for cerebrovascular disease was available from the New South Wales Brain Banks. Quantified frontal cortex amyloid beta (Aβ) and tau proteins plus Alzheimer's neuropathologic change scores were analyzed.

RESULTS

Univariate analyses found no difference in amounts of AD proteins in the frontal cortex between medication users, but multivariate analyses showed that antihypertensive medication use was associated with a less extensive spread of AD proteins throughout the brain.

DISCUSSION

The heterogeneous nature of the antihypertensive medications is consistent with downstream beneficial effects of blood pressure lowering and/or management being associated with the reduced spreading of AD pathology observed.

Molecular docking of C-Jun-N-Terminal Kinase (Jnk) with amino-pyrimidine derivatives

It is of interest to document the molecular docking of C-Jun-N-Terminal Kinase (Jnk) (known structure with PDB ID: 1PMN) with amino-pyrimidine derivatives in the context of Alzheimer's Disease (AD). We report the optimal binding features (binding energy, interacting residues, inter atomic hydrogen bonding patterns) of 11 amino-pyrimidine derivatives with Jnk for further consideration.

Determinants of incident dementia in different old age groups: results of the prospective AgeCoDe/AgeQualiDe study

OBJECTIVES

To examine the impact of determinants of incident dementia in three different old age groups (75-79, 80-84, 85+years) in Germany.

DESIGN

Multicenter prospective AgeCoDe/AgeQualiDe cohort study with baseline and nine follow-up assessments at 1.5-year intervals.

SETTING

Primary care medical record registry sample.

PARTICIPANTS

General practitioners' (GPs) patients aged 75+years at baseline.

MEASUREMENTS

Conduction of standardized interviews including neuropsychological assessment and collection of GP information at each assessment wave. We used age-stratified competing risk regression models (accounting for the competing event of mortality) to assess determinants of incident dementia and age-stratified ordinary least square regressions to quantify the impact of identified determinants on the age at dementia onset.

RESULTS

Among 3027 dementia-free GP patients, n = 704 (23.3%) developed dementia during the 13-year study period. Worse cognitive performance and subjective memory decline with related worries at baseline, and the APOE ε4 allele were associated independently with increased dementia risk in all three old age groups. Worse cognitive performance at baseline was also associated with younger age at dementia onset in all three age groups. Other well-known determinants were associated with dementia risk and age at dementia onset only in some or in none of the three old age groups.

CONCLUSIONS

This study provides further evidence for the age-specific importance of determinants of incident dementia in old age. Such specifics have to be considered more strongly particularly with regard to potential approaches of early detection and prevention of dementia.

Anti-Aβ Antibodies and Cerebral Amyloid Angiopathy Complications

Cerebral amyloid angiopathy (CAA) corresponds to the deposition of amyloid material in the cerebral vasculature, leading to structural modifications of blood vessel walls. The most frequent form of sporadic CAA involves fibrillar β-amyloid peptide (Aβ) deposits, mainly the 40 amino acid form (Aβ_1-40), which are commonly found in the elderly with or without Alzheimer's disease. Sporadic CAA usually remains clinically silent. However, in some cases, acute complications either hemorrhagic or inflammatory can occur. Similar complications occurred after active or passive immunization against Aβ in experimental animal models exhibiting CAA, and in subjects with Alzheimer's disease during clinical trials. The triggering of these adverse events by active immunization and monoclonal antibody administration in CAA-bearing individuals suggests that analogous mechanisms could be involved during spontaneous CAA complications, drawing particular attention to the role of anti-Aβ antibodies. However, antibodies that react with several monomeric and aggregated forms of Aβ spontaneously occur in virtually all human individuals, hence being part of the "natural antibody" repertoire. Natural antibodies are usually described as having low-affinity and high cross-reactivity toward microbial components and autoantigens. Although frequently of the IgM class, they also belong to IgG and IgA isotypes. They likely display homeostatic functions and protective roles in aging. Until recently, the peculiar properties of these natural antibodies have hindered proper analysis of the Aβ-reactive antibody repertoire and the study of their implication in CAA complications. Herein, we review and comment the evidences of an auto-immune nature of spontaneous CAA complications, and discuss implications for forthcoming research and clinical practice.

Limbic-predominant age-related TDP-43 encephalopathy (LATE): consensus working group report

We describe a recently recognized disease entity, limbic-predominant age-related TDP-43 encephalopathy (LATE). LATE neuropathological change (LATE-NC) is defined by a stereotypical TDP-43 proteinopathy in older adults, with or without coexisting hippocampal sclerosis pathology. LATE-NC is a common TDP-43 proteinopathy, associated with an amnestic dementia syndrome that mimicked Alzheimer's-type dementia in retrospective autopsy studies. LATE is distinguished from frontotemporal lobar degeneration with TDP-43 pathology based on its epidemiology (LATE generally affects older subjects), and relatively restricted neuroanatomical distribution of TDP-43 proteinopathy. In community-based autopsy cohorts, ∼25% of brains had sufficient burden of LATE-NC to be associated with discernible cognitive impairment. Many subjects with LATE-NC have comorbid brain pathologies, often including amyloid-β plaques and tauopathy. Given that the 'oldest-old' are at greatest risk for LATE-NC, and subjects of advanced age constitute a rapidly growing demographic group in many countries, LATE has an expanding but under-recognized impact on public health. For these reasons, a working group was convened to develop diagnostic criteria for LATE, aiming both to stimulate research and to promote awareness of this pathway to dementia. We report consensus-based recommendations including guidelines for diagnosis and staging of LATE-NC. For routine autopsy workup of LATE-NC, an anatomically-based preliminary staging scheme is proposed with TDP-43 immunohistochemistry on tissue from three brain areas, reflecting a hierarchical pattern of brain involvement: amygdala, hippocampus, and middle frontal gyrus. LATE-NC appears to affect the medial temporal lobe structures preferentially, but other areas also are impacted. Neuroimaging studies demonstrated that subjects with LATE-NC also had atrophy in the medial temporal lobes, frontal cortex, and other brain regions. Genetic studies have thus far indicated five genes with risk alleles for LATE-NC: GRN, TMEM106B, ABCC9, KCNMB2, and APOE. The discovery of these genetic risk variants indicate that LATE shares pathogenetic mechanisms with both frontotemporal lobar degeneration and Alzheimer's disease, but also suggests disease-specific underlying mechanisms. Large gaps remain in our understanding of LATE. For advances in prevention, diagnosis, and treatment, there is an urgent need for research focused on LATE, including in vitro and animal models. An obstacle to clinical progress is lack of diagnostic tools, such as biofluid or neuroimaging biomarkers, for ante-mortem detection of LATE. Development of a disease biomarker would augment observational studies seeking to further define the risk factors, natural history, and clinical features of LATE, as well as eventual subject recruitment for targeted therapies in clinical trials.

Structure Based Design and Molecular Docking Studies for Phosphorylated Tau Inhibitors in Alzheimer's Disease

The purpose of our study is to identify phosphorylated tau (p-tau) inhibitors. P-tau has recently received great interest as a potential drug target in Alzheimer’s disease (AD). The continuous failure of Aβ-targeted therapeutics recommends an alternative drug target to treat AD. There is increasing evidence and growing awareness of tau, which plays a central role in AD pathophysiology, including tangles formation, abnormal activation of phosphatases/kinases, leading p-tau aggregation in AD neurons. In the present study, we performed computational pharmacophore models, molecular docking, and simulation studies for p-tau in order to identify hyperphosphorylated sites. We found multiple serine sites that altered the R1/R2 repeats flanking sequences in the tau protein, affecting the microtubule binding ability of tau. The ligand molecules exhibited the p-O ester scaffolds with inhibitory and/or blocking actions against serine residues of p-tau. Our molecular docking results revealed five ligands that showed high docking scores and optimal protein-ligand interactions of p-tau. These five ligands showed the best pharmacokinetic and physicochemical properties, including good absorption, distribution, metabolism, and excretion (ADME) and admetSAR toxicity tests. The p-tau pharmacophore based drug discovery models provide the comprehensive and rapid drug interventions in AD, and tauopathies are expected to be the prospective future therapeutic approach in AD.