NIA-AA Alzheimer's Disease Framework: Clinical Characterization of Stages

Symptomatic Clusters Related to Amyloid Positivity in Cognitively Unimpaired Individuals

Background

The NIA-AA Research Framework on Alzheimer's disease (AD) proposes a transitional stage (stage 2) characterized by subtle cognitive decline, subjective cognitive decline (SCD) and mild neurobehavioral symptoms (NPS).

Objective

To identify participant clusters based on stage 2 features and assess their association with amyloid positivity in cognitively unimpaired individuals.

Methods

We included baseline data of N = 338 cognitively unimpaired participants from the DELCODE cohort with data on cerebrospinal fluid biomarkers for AD. Classification into the AD continuum (i.e., amyloid positivity, A+) was based on Aβ42/40 status. Neuropsychological test data were used to assess subtle objective cognitive dysfunction (OBJ), the subjective cognitive decline interview (SCD-I) was used to detect SCD, and the Neuropsychiatric Inventory Questionnaire (NPI-Q) was used to assess NPS. A two-step cluster analysis was carried out and differences in AD biomarkers between clusters were analyzed.

Results

We identified three distinct participant clusters based on presented symptoms. The highest rate of A+ participants (47.6% ) was found in a cluster characterized by both OBJ and SCD. A cluster of participants that presented with SCD and NPS (A+:26.6% ) and a cluster of participants with overall few symptoms (A+:19.7% ) showed amyloid positivity in a range that was not higher than the expected A+ rate for the age group. Across the full sample, participants with a combination of SCD and OBJ in the memory domain showed a lower Aβ42/ptau181 ratio compared to those with neither SCD nor OBJ.

Conclusions

The cluster characterized by participants with OBJ and concomitant SCD was enriched for amyloid pathology.

Category Switching Test: A Brief Amyloid-β-Sensitive Assessment Tool for Mild Cognitive Impairment

The Category Switching Test (CaST) is a verbal fluency test with active semantic category switching. This study aimed to explore the association between CaST performance and brain amyloid-β (Aβ) burden in patients with mild cognitive impairment (MCI) and the neurofunctional mechanisms. A total of 112 participants with MCI underwent Florbetapir positron emission tomography, resting-state functional magnetic resonance imaging, and a neuropsychological test battery. The high Aβ burden group had worse CaST performance than the low-burden group. CaST score and left middle temporal gyrus fractional amplitude of low-frequency fluctuations (fALFF) related inversely to the global Florbetapir standardized uptake value rate. Functional connectivity between the left middle temporal gyrus and frontal lobe decreased widely and correlated with CaST score in the high Aβ burden group. Thus, CaST score and left middle temporal gyrus fALFF were valuable in discriminating high Aβ burden. CaST might be useful in screening for MCI with high Aβ burden.

Biomarker-based staging of Alzheimer disease: rationale and clinical applications

Disease staging, whereby the spatial extent and load of brain pathology are used to estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard neuropathological diagnosis of AD. Current in vivo diagnostic frameworks for AD are based on abnormal concentrations of amyloid-β and tau in the cerebrospinal fluid or on PET scans, and breakthroughs in molecular imaging have opened up the possibility of in vivo staging of AD. Focusing on the key principles of disease staging shared across several areas of medicine, this Review highlights the potential for in vivo staging of AD to transform our understanding of preclinical AD, refine enrolment criteria for trials of disease-modifying therapies and aid clinical decision-making in the era of anti-amyloid therapeutics. We provide a state-of-the-art review of recent biomarker-based AD staging systems and highlight their contributions to the understanding of the natural history of AD. Furthermore, we outline hypothetical frameworks to stage AD severity using more accessible fluid biomarkers. In addition, by applying amyloid PET-based staging to recently published anti-amyloid therapeutic trials, we highlight how biomarker-based disease staging frameworks could illustrate the numerous pathological changes that have already taken place in individuals with mildly symptomatic AD. Finally, we discuss challenges related to the validation and standardization of disease staging and provide a forward-looking perspective on potential clinical applications.

"Evaluation of neprilysin activity in Adipose-Derived stem cells from Alzheimer's disease patients"

INTRODUCTION

The antibody drugs targeting β-amyloid in Alzheimer's disease pose risks of inflammation and vascular damage. It is known that neprilysin, an endogenous enzyme responsible for β-amyloid degradation, is reduced in areas with β-amyloid deposition. Supplementation of neprilysin could potentially contribute to Alzheimer's disease treatment. When considering the use of adipose tissue-derived stem cells (ADSCs) for Alzheimer's disease therapy, it is crucial to ensure that Alzheimer's disease patient-derived ADSCs maintain neprilysin activity. If so, the use of autologous ADSCs may lead to a treatment with minimal risks of rejection or infection. Therefore, we investigated the neprilysin activity in Alzheimer's disease patient-derived adipose tissue-derived stem cells to assess their potential in Alzheimer's disease treatment.

METHODS

Five Alzheimer's disease patients (MSC1-5) and two Chronic Obstructive Pulmonary Disease (COPD) patients (MSC6-7) were enrolled. ADSCs were cultured for 6 days with varying seeding densities. On the 3rd day, the medium was replaced, and on the 6th day, ADSCs were harvested. Cells were stained for PE-Cy7 Mouse IgG1 κ Isotype control and PE-Cy Mouse Anti-Human CD10, and CD10 expression was assessed by flow cytometry. Ethical approval and informed consent were obtained.

RESULTS

Neprilysin activity, crucial for β-amyloid degradation, was assessed in ADSCs. Positivity rates for CD10 expression in ADSCs from Alzheimer's patients were consistently high: 99.6%, 99.5%, 99.9%, 99.3%, 99.8%, and 100.0%. Control ADSCs from COPD patients (MSC6-7) exhibited comparable positivity rates. Flow cytometry plots for all seven cases are presented in Figures 1-7.

DISCUSSION

This study confirms the presence and maintenance of neprilysin activity in ADSCs from Alzheimer's disease patients. The high positivity rates for CD10 expression in these cells suggest that neprilysin, a key enzyme in β-amyloid degradation, remains active. The implications are significant, as ADSCs offer immune-compatible and low infection risk advantages. The study underscores the potential of autologous ADSCs as a therapeutic approach in Alzheimer's disease. Their ability to naturally harbor neprilysin activity, coupled with their safety profile, makes them a promising candidate for further exploration. While acknowledging the need for larger, more diverse cohorts and long-term studies, these findings contribute to the growing body of evidence supporting the development of stem cell-based interventions in Alzheimer's disease treatment.

Predicting the Rapid Progression of Mild Cognitive Impairment by Intestinal Flora and Blood Indicators through Machine Learning Method

INTRODUCTION

The aim of the work was to establish a prediction model of mild cognitive impairment (MCI) progression based on intestinal flora by machine learning method.

METHOD

A total of 1,013 patients were recruited, in which 87 patients with MCI finished a two-year follow-up. To establish a prediction model, 61 patients were randomly divided into a training set and 26 patients were divided into a testing set. A total of 121 features including demographic characteristics, hematological indicators, and intestinal flora abundance were analyzed.

RESULTS

Of the 87 patients who finished a two-year follow-up, 44 presented rapid progression. Model 1 was established based on 121 features with the accuracy 85%, sensitivity 85%, and specificity 83%. Model 2 was based on the first fifteen features of model 1 (triglyceride, uric acid, alanine transaminase, F-Clostridiaceae, G-Megamonas, S-Megamonas, G-Shigella, G-Shigella, S-Shigella, average hemoglobin concentration, G-Alistipes, S-Collinsella, median cell count, average hemoglobin volume, low-density lipoprotein), with the accuracy 97%, sensitivity 92%, and specificity 100%. Model 3 was based on the first ten features of model 1, with the accuracy 97%, sensitivity 86%, and specificity 100%. Other models based on the demographic characteristics, hematological indicators, or intestinal flora abundance features presented lower sensitivity and specificity.

CONCLUSION

The 15 features (including intestinal flora abundance) could establish an effective model for predicting rapid MCI progression.

Developing a Staging Scheme for Essential Tremor: A Discussion of Organizing Principles

Essential tremor (ET) is a chronic, progressive neurological disease that may negatively affect patients' lives. While there has been considerable progress in ET research, some fundamental issues remain unaddressed. One such issue is disease staging. Staging schemes have inherent value and are part of the dialogue that clinicians have with other movement disorders patients. We highlight the value of and challenges with developing a staging system for ET and organize a discussion around the potential steps in developing such a system. Diseases for which there are staging schemes generally have a number of shared characteristics. ET has numerous features that would lend themselves to a staging scheme: emerging evidence supporting the existence of a premotor phase of disease, insidious onset, progressive worsening of arm tremor, spread of tremor to other body regions, the observation that patients seem to be at increased risk for other conditions within the same organ (i.e., emergence of Parkinson's disease and Alzheimer's disease in excessive numbers of ET patients), pathological changes in the cerebellum whose evolution can be ordered from (i) those that compromise the physical integrity and physiological function of Purkinje cells, (ii) subsequent changes that are reparative and regenerative, and (iii) eventual cell death. Challenges to formulating a staging scheme are the absence of both a biological marker and an "end stage" of disease. The sum of combined evidence suggests that a staging scheme would be of value. We provide initial thoughts as to how to begin to structure such a staging scheme.

The application of saccades to assess cognitive impairment among older adults: a systematic review and meta-analysis

BACKGROUND

Saccade is a novel and feasible method for cognition assessment and has potential to screen older people with cognitive impairment.

OBJECTIVES

To systematically summarize the evidence and determine whether different saccade parameters can effectively identify patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).

METHODS

English and Chinese databases were searched until 19 April 2022. Studies analyzing saccade parameters in older adults with normal cognition, MCI, or AD were included. Two researchers independently performed the screening, data extraction, and quality appraisal. Meta-analyses were conducted and standard mean differences and 95% confidence intervals were estimated with a random effects model.

RESULTS

Thirty-five studies were included, and 26 studies were pooled for the meta-analysis. The results demonstrated that patients with cognitive impairment exhibited longer latency and lower accuracy rates in the prosaccade and antisaccade tasks, along with lower corrected error rates in the antisaccade tasks. However, the pooled results for antisaccades were more stable, providing the ability to distinguish patients with cognitive impairment among older adults. The results of the subgroup analyses revealed that only the accuracy rates of the antisaccades differed significantly between people with MCI and AD. Regarding the differences between older adults with normal cognition and those with MCI, the effect sizes of latency and the accuracy rates of saccades as well as the corrected error rates of antisaccades were significant.

CONCLUSIONS

Saccades, especially antisaccades, are a potential screening and assessment tool for distinguishing older adults with MCI or AD from those with normal cognition.

Operationalizing selection criteria for clinical trials in Alzheimer's disease: Biomarker and clinical considerations

Alzheimer's disease (AD) staging criteria lack standardized, empirical description. Well-defined AD staging criteria are an important consideration in protocol design, influencing a more standardized inclusion/exclusion criteria and defining what constitutes meaningful differentiation among the stages. However, many trials are being designed on the basis of biomarker features and the two need to be coordinated. The Alzheimer's Association Research Roundtable (AARR) Spring 2021 meeting discussed the implementation of preclinical AD staging criteria, and provided recommendations for how they may best be incorporated into clinical trials research. Discussion also included what currently available tools for global clinical trials may best define populations in preclinical AD trials, and if are we able to differentiate preclinical from clinical stages of the disease. Well-defined AD staging criteria are key to improving early detection, diagnostics, clinical trial enrollment, and identifying statistically significant clinical changes, and researchers discussed how emerging blood biomarkers may help with more efficient screening in preclinical stages.

Serum sirtuin1: a potential blood biomarker for early diagnosis of Alzheimer's disease

BACKGROUND

Sirtuin 1, a nicotinamide adenine dinucleotide-dependent deacetylase that is highly expressed in the hippocampus and anterior cortex tissues related to Alzheimer's Disease pathology, can cross the blood-brain barrier and is a promising biomarker.

METHODS

A 1:1:1 case-control study was conducted and serum fasting blood glucose, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, SIRT1, IL-6, Aβ1-42, T-tau and P-tau-181 levels were evaluated in blood samples of 26 patients form the Alzheimer's Disease group, 26 patients form the mild cognitive impairment group, and 26 individuals form the normal control group. Receiver operator characteristic curves were used to evaluate the diagnostic significance.

RESULTS

Serum SIRT1 level was significantly down-regulated in the mild cognitive impairment patients and Alzheimer's Disease patients compared with that in the normal control group (P<0.05). ROC curve analysis demonstrated that SIRT1 was a promising biomarker to distinguish Alzheimer's Disease patients from the mild cognitive impairment patients and the normal control group. In addition, SIRT1 was estimated to perform well in the diagnosis of Alzheimer's Disease ([AUC] = 0.742).

CONCLUSIONS

In summary, the present study suggested that serum SIRT1 might be an early promising diagnostic biomarker for Alzheimer's Disease.

New Community and Sociohealth Challenges Arising from the Early Diagnosis of Mild Cognitive Impairment (MCI)

Population aging increases the risk of developing neurodegenerative diseases that cause cognitive impairment. Advances in clinical practice and greater social awareness of the importance of cognitive impairment have led to an increase in the number of people with early diagnosis, predementia. Increasing access to biomarkers to assess whether Alzheimer's disease (AD) is the underlying cause of mild cognitive impairment (MCI) has undoubted clinical benefits (access to potentially disease-modifying treatments, among others) but is also responsible for new social-health care challenges. Understanding the psychosocial impact of a diagnosis of MCI due to AD or another neurodegenerative disease is essential to create future strategies to reduce the emotional overload of patients, their risk of discrimination and stigmatization, and to favor their social inclusion. We present a narrative review of the diagnostic process of mild cognitive impairment in clinical practice, with a holistic person-centered approach, and discuss the implications of such diagnosis (benefits and risks) and strategies on how to address them.

A graph theoretic approach to neurodegeneration: five data-driven neuropsychological subtypes in mild cognitive impairment

Mild cognitive Impairment (MCI) is notoriously heterogenous in terms of clinical presentation, neuroimaging correlates, and subsequent progression. Predicting who will progress to dementia, which type of dementia, and over what timeframe is challenging. Previous work has attempted to identify MCI subtypes using neuropsychological measures in an effort to address this challenge; however, there is no consensus on approach, which may account for some of the variability. Using a hierarchical community detection approach, we examined cognitive subtypes within an MCI sample (from the Alzheimer's Disease Neuroimaging Initiative [ADNI] study). We then examined whether these subtypes were related to biomarkers (e.g., cortical volumes, fluorodeoxyglucose (FDG)-positron emission tomography (PET) hypometabolism) or clinical progression. We identified five communities (i.e., cognitive subtypes) within the MCI sample: 1) predominantly memory impairment, 2) predominantly language impairment, 3) cognitively normal, 4) multidomain, with notable executive dysfunction, 5) multidomain, with notable processing speed impairment. Community membership was significantly associated with 1) cortical volume in the hippocampus, entorhinal cortex, and fusiform cortex; 2) FDG PET hypometabolism in the posterior cingulate, angular gyrus, and inferior/middle temporal gyrus; and 3) conversion to dementia at follow up. Overall, community detection as an approach appears a viable method for identifying unique cognitive subtypes in a neurodegenerative sample that were linked to several meaningful biomarkers and modestly with progression at one year follow up.

Clinical Characteristics and Follow-up Assessment in Patients Diagnosed With Alzheimer's Dementia Through Regional Dementia Centers and Conventional Hospital System

BACKGROUND

The rapidly increasing socioeconomic strain caused by dementia represents a significant public health concern. Regional dementia centers (RDCs) have been established nationwide, and they aim to provide timely screening and diagnosis of dementia. This study investigated the clinical characteristics and progression of patients diagnosed with Alzheimer's dementia (AD), who underwent treatment in RDCs or conventional community-based hospital systems.

METHODS

This retrospective single-center cohort study included patients who were diagnosed with AD between January 2019 and March 2022. This study compared two groups of patients: the hospital group, consisting of patients who presented directly to the hospital, and the RDC group, those who were referred to the hospital from the RDCs in Pohang city. The clinical courses of the patients were monitored for a year after AD diagnosis.

RESULTS

A total of 1,209 participants were assigned to the hospital (n = 579) or RDC group (n = 630). The RDC group had a mean age of 80.1 years ± 6.6 years, which was significantly higher than that of the hospital group (P < 0.001). The RDC group had a higher proportion of females (38.3% vs. 31.9%; P = 0.022), higher risk for alcohol consumption (12.4% vs. 3.3%; P < 0.001), and greater number of patients who discontinued treatment 1 year after diagnosis (48.3% vs. 39.0%; P = 0.001). In the linear regression model, the RDC group was independently associated with the clinical dementia rating sum of boxes increment (β = 22.360, R²\n = 0.048, and P < 0.001).

CONCLUSION

Patients in the RDC group were older, had more advanced stages of conditions, and exhibited a more rapid rate of cognitive decline than patients diagnosed through the conventional hospital system. Our results suggested that RDC contributed to the screening of AD in a local region, and further nationwide study with the RDC database of various areas of Korea is needed.

Optimal combinations of CSF biomarkers for predicting cognitive decline and clinical conversion in cognitively unimpaired participants and mild cognitive impairment patients: A multi-cohort study

INTRODUCTION

Our objective was determining the optimal combinations of cerebrospinal fluid (CSF) biomarkers for predicting disease progression in Alzheimer's disease (AD) and other neurodegenerative diseases.

METHODS

We included 1,983 participants from three different cohorts with longitudinal cognitive and clinical data, and baseline CSF levels of Aβ42, Aβ40, phosphorylated tau at threonine-181 (p-tau), neurofilament light (NfL), neurogranin, α-synuclein, soluble triggering receptor expressed on myeloid cells 2 (sTREM2), glial fibrillary acidic protein (GFAP), YKL-40, S100b, and interleukin 6 (IL-6) (Elecsys NeuroToolKit).

RESULTS

Change of modified Preclinical Alzheimer's Cognitive Composite (mPACC) in cognitively unimpaired (CU) was best predicted by p-tau/Aβ42 alone (R2 ≥ 0.31) or together with NfL (R2  = 0.25), while p-tau/Aβ42 (R2 ≥ 0.19) was sufficient to accurately predict change of the Mini-Mental State Examination (MMSE) in mild cognitive impairment (MCI) patients. P-tau/Aβ42 (AUC ≥ 0.87) and p-tau/Aβ42 together with NfL (AUC ≥ 0.75) were the best predictors of conversion to AD and all-cause dementia, respectively.

DISCUSSION

P-tau/Aβ42 is sufficient for predicting progression in AD, with very high accuracy. Adding NfL improves the prediction of all-cause dementia conversion and cognitive decline.

Three potential neurovascular pathways driving the benefits of mindfulness meditation for older adults

Mindfulness meditation has been shown to be beneficial for a range of different health conditions, impacts brain function and structure relatively quickly, and has shown promise with aging samples. Functional magnetic resonance imaging metrics provide insight into neurovascular health which plays a key role in both normal and pathological aging processes. Experimental mindfulness meditation studies that included functional magnetic resonance metrics as an outcome measure may point to potential neurovascular mechanisms of action relevant for aging adults that have not yet been previously examined. We first review the resting-state magnetic resonance studies conducted in exclusively older adult age samples. Findings from older adult-only samples are then used to frame the findings of task magnetic resonance imaging studies conducted in both clinical and healthy adult samples. Based on the resting-state studies in older adults and the task magnetic resonance studies in adult samples, we propose three potential mechanisms by which mindfulness meditation may offer a neurovascular therapeutic benefit for older adults: (1) a direct neurovascular mechanism via increased resting-state cerebral blood flow; (2) an indirect anti-neuroinflammatory mechanism via increased functional connectivity within the default mode network, and (3) a top-down control mechanism that likely reflects both a direct and an indirect neurovascular pathway.

The Use of F-18 FDG PET-Based Cognitive Reserve to Evaluate Cognitive Decline in Alzheimer's Disease, Independent of Educational Influence

Background and Objectives: The optimal assessment of cognitive function, including the impact of education, is crucial in managing Alzheimer's disease (AD). This study aimed to evaluate the role of cognitive reserve (CR), represented by the metabolic status of regions of the cerebral cortex, to evaluate cognitive decline considering the educational attainment of patients with AD. Materials and Methods: We used data from the Alzheimer's Disease Neuroimaging Initiative database, and selected 124 patients who underwent both baseline F-18 fluorodeoxyglucose (FDG) and F-18 florbetaben (FBB) positron emission tomography (PET) scans. Demographics, cognitive function variables (Clinical Dementia Rating-Sum of Boxes [CDR]; AD Assessment Scale 11/13 [ADAS11/13] Mini-Mental State Examination [MMSE]), and the average standardized uptake value ratio (SUVR) of cerebral cortex regions to those of the cerebellum were obtained from the data. The participants' education level was divided into low and high education subgroups using four cut-offs of 12, 14, 16, and 18 years of educational attainment (G12, G14, G16, and G18, respectively). Demographic and cognitive function variables were compared between the two subgroups in each of the four groups, and their correlations with the SUVRs were evaluated. Results: There was no significant difference between the high and low education subgroups in each of the four groups, except for ADAS11/13 and MMSE in G14 and age in G16. The SUVRs of FDG PET (FDG_SUVR) were significantly correlated with CDR, ADAS11/13, and MMSE scores. FDG_SUVR showed different trajectories of neurodegeneration between the low and high education groups. Conclusions: FDG_SUVR correlated moderately but significantly with neuropsychological test results, without being influenced by education level. Therefore, FDG PET may reflect CR independent of education level, and therefore could be a reliable tool to evaluate cognitive decline in AD.

Subjective cognitive decline and stage 2 of Alzheimer disease in patients from memory centers

INTRODUCTION

It is uncertain whether subjective cognitive decline (SCD) in individuals who seek medical help serves the identification of the initial symptomatic stage 2 of the Alzheimer's disease (AD) continuum.

METHODS

Cross-sectional and longitudinal data from the multicenter, memory clinic-based DELCODE study.

RESULTS

The SCD group showed slightly worse cognition as well as more subtle functional and behavioral symptoms than the control group (CO). SCD-A+ cases (39.3% of all SCD) showed greater hippocampal atrophy, lower cognitive and functional performance, and more behavioral symptoms than CO-A+. Amyloid concentration in the CSF had a greater effect on longitudinal cognitive decline in SCD than in the CO group.

DISCUSSION

Our data suggests that SCD serves the identification of stage 2 of the AD continuum and that stage 2, operationalized as SCD-A+, is associated with subtle, but extended impact of AD pathology in terms of neurodegeneration, symptoms and clinical progression.

Machine-Based Learning Shifting to Prediction Model of Deteriorative MCI Due to Alzheimer's Disease - A Two-Year Follow-Up Investigation

OBJECTIVE

The aim of the present work was to investigate the features of the elderly population aged ≥65 yrs and with deteriorative mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to establish a prediction model.

METHODS

A total of 105 patients aged ≥65 yrs and with MCI were followed up, with a collection of 357 features, which were derived from the demographic characteristics, hematological indicators (serum Aβ1-40, Aβ1-42, P-tau and MCP-1 levels, APOE gene), and multimodal brain Magnetic Resonance Imaging (MRI) imaging indicators of 116 brain regions (ADC, FA and CBF values). Cognitive function was followed up for 2 yrs. Based on the Python platform Anaconda, 105 patients were randomly divided into a training set (70%) and a test set (30%) by analyzing all features through a random forest algorithm, and a prediction model was established for the form of rapidly deteriorating MCI.

RESULTS

Of the 105 patients enrolled, 41 deteriorated, and 64 did not come within 2 yrs. Model 1 was established based on demographic characteristics, hematological indicators and multi-modal MRI image features, the accuracy of the training set being 100%, the accuracy of the test set 64%, sensitivity 50%, specificity 67%, and AUC 0.72. Model 2 was based on the first five features (APOE4 gene, FA value of left fusiform gyrus, FA value of left inferior temporal gyrus, FA value of left parahippocampal gyrus, ADC value of right calcarine fissure as surrounding cortex), the accuracy of the training set being 100%, the accuracy of the test set 85%, sensitivity 91%, specificity 80% and AUC 0.96. Model 3 was based on the first four features of Model 1, the accuracy of the training set is 100%, the accuracy of the test set 97%, sensitivity100%, specificity 95% and AUC 0.99. Model 4 was based on the first three characteristics of Model 1, the accuracy of the training set being 100%, the accuracy of the test set 94%, sensitivity 92%, specificity 94% and AUC 0.96. Model 5 was based on the hematological characteristics, the accuracy of the training set is 100%, the accuracy of the test set 91%, sensitivity 100%, specificity 88% and AUC 0.97. The models based on the demographic characteristics, imaging characteristics FA, CBF and ADC values had lower sensitivity and specificity.

CONCLUSION

Model 3, which has four important predictive characteristics, can predict the rapidly deteriorating MCI due to AD in the community.

Tau levels are higher in objective subtle cognitive decline but not subjective memory complaint

BACKGROUND

The 2018 NIA-AA Alzheimer's Disease (AD) Research Framework states that subtle cognitive decline in cognitively unimpaired individuals can be measured by subjective reports or evidence of objective decline on neuropsychological measures. Both subjective memory complaint (SMC) and objective subtle cognitive decline (Obj-SCD) have been shown to be associated with future cognitive decline and AD biomarkers. We examined whether there are differences in tau PET levels between (a) SMC- vs. SMC+ participants, (b) Obj-SCD- vs. Obj-SCD+ participants, and (c) participants with overlapping vs. discrepant SMC and Obj-SCD classifications.

METHODS

Cognitively unimpaired participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 236) were classified at baseline as positive or negative for SMC (SMC- n = 77; SMC+ n = 159) based on the first 12 items of the Cognitive Change Index and/or classified as positive or negative for Obj-SCD (Obj-SCD- n = 173; Obj-SCD+ n = 63) based on previously defined neuropsychological criteria. Analyses of covariance, adjusting for age, sex, APOE ε4 carrier status, and pulse pressure, examined the group differences in tau PET (AV-1451) using a composite standardized uptake variable ratio (SUVR) for regions consistent with Braak stage III/IV. The chi-squared tests examined the tau positivity rates across the groups.

RESULTS

Obj-SCD+ participants had higher tau continuous SUVR levels (p = .035, ηp2 = .019) and higher rates of tau positivity (15.8% Obj-SCD- vs. 30.2% Obj-SCD+) than Obj-SCD- participants. Neither tau levels (p = .381, ηp2 = .003) nor rates of tau positivity (18.2% SMC- and 20.1% SMC+) differed between the SMC groups. There was very little agreement between SMC and Obj-SCD classifications (42%; κ = 0.008, p = .862). Participants who were Obj-SCD+ without SMC had the highest tau PET levels and differed from participants who were SMC+ without Obj-SCD (p = .022). Tau levels in participants with both SMC and Obj-SCD did not differ from those with only Obj-SCD (p = .216). Tau positivity rates across the SMC-/Obj-SCD-, SMC+/Obj-SCD-, SMC-/Obj-SCD+, and SMC+/Obj-SCD+ groups were 10.5%, 18.1%, 40.0%, and 25.6%, respectively.

CONCLUSION

Participants with Obj-SCD had a greater tau PET burden than those without Obj-SCD, but SMC was not associated with higher tau levels. The combination of SMC and Obj-SCD did not have higher tau levels than Obj-SCD alone. Findings add to the evidence that the Obj-SCD classification is associated with AD biomarkers and faster cognitive decline in ADNI participants, but further work is needed to validate this approach in more representative/diverse cohorts.

Associations of digital neuro-signatures with molecular and neuroimaging measures of brain resilience: The altoida large cohort study

Background

Mixed results in the predictive ability of traditional biomarkers to determine cognitive functioning and changes in older adults have led to misdiagnosis and inappropriate treatment plans to address mild cognitive impairment and dementia among older adults. To address this critical gap, the primary goal of the current study is to investigate whether a digital neuro signature (DNS-br) biomarker predicted global cognitive functioning and change over time relative among cognitively impaired and cognitive healthy older adults. The secondary goal is to compare the effect size of the DNS-br biomarker on global cognitive functioning compared to traditional imaging and genomic biomarkers. The tertiary goal is to investigate which demographic and clinical factors predicted DNS-br in cognitively impaired and cognitively healthy older adults.

Methods

We conducted two experiments (Study A and Study B) to assess DNS for brain resilience (DNS-br) against the established FDG-PET brain imaging signature for brain resilience, based on a 10 min digital cognitive assessment tool. Study A was a semi-naturalistic observational study that included 29 participants, age 65+, with mild to moderate mild cognitive impairment and AD diagnosis. Study B was also a semi-naturalistic observational multicenter study which included 496 participants (213 mild cognitive impairment (MCI) and 283 cognitively healthy controls (HC), a total of 525 participants-cognitively healthy (n = 283) or diagnosed with MCI (n = 213) or AD (n = 29).

Results

DNS-br total score and majority of the 11 DNS-br neurocognitive subdomain scores were significantly associated with FDG-PET resilience signature, PIB ratio, cerebral gray matter and white matter volume after adjusting for multiple testing. DNS-br total score predicts cognitive impairment for the 80+ individuals in the Altoida large cohort study. We identified a significant interaction between the DNS-br total score and time, indicating that participants with higher DNS-br total score or FDG-PET in the resilience signature would show less cognitive decline over time.

Conclusion

Our findings highlight that a digital biomarker predicted cognitive functioning and change, which established biomarkers are unable to reliably do. Our findings also offer possible etiologies of MCI and AD, where education did not protect against cognitive decline.

Alzheimer's Disease Blood Biomarkers Associated With Neuroinflammation as Therapeutic Targets for Early Personalized Intervention

The definitive diagnosis of Alzheimer's Disease (AD) without the need for neuropathological confirmation remains a challenge in AD research today, despite efforts to uncover the molecular and biological underpinnings of the disease process. Furthermore, the potential for therapeutic intervention is limited upon the onset of symptoms, providing motivation for studying and treating the AD precursor mild cognitive impairment (MCI), the prodromal stage of AD instead. Applying machine learning classification to transcriptomic data of MCI, AD, and cognitively normal (CN) control patients, we identified differentially expressed genes that serve as biomarkers for the characterization and classification of subjects into MCI or AD groups. Predictive models employing these biomarker genes exhibited good classification performances for CN, MCI, and AD, significantly above random chance. The PI3K-Akt, IL-17, JAK-STAT, TNF, and Ras signaling pathways were also enriched in these biomarker genes, indicating their diagnostic potential and pathophysiological roles in MCI and AD. These findings could aid in the recognition of MCI and AD risk in clinical settings, allow for the tracking of disease progression over time in individuals as part of a therapeutic approach, and provide possible personalized drug targets for early intervention of MCI and AD.

Alzheimer Disease

PURPOSE OF REVIEW

Alzheimer disease (AD) is the most common cause of dementia in adults (mid to late life), highlighting the importance of understanding the risk factors, clinical manifestations, and recent developments in diagnostic testing and therapeutics.

RECENT FINDINGS

Advances in fluid (CSF and blood-based) and imaging biomarkers are allowing for a more precise and earlier diagnosis of AD (relative to non-AD dementias) across the disease spectrum and in patients with atypical clinical features. Specifically, tau- and amyloid-related AD pathologic changes can now be measured by CSF, plasma, and positron emission tomography (PET) with good precision. Additionally, a better understanding of risk factors for AD has highlighted the need for clinicians to address comorbidities to maximize prevention of cognitive decline in those at risk or to slow decline in patients who are symptomatic. Recent clinical trials of amyloid-lowering drugs have provided not only some optimism that amyloid reduction or prevention may be beneficial but also a recognition that addressing additional targets will be necessary for significant disease modification.

SUMMARY

Recent developments in fluid and imaging biomarkers have led to the improved understanding of AD as a chronic condition with a protracted presymptomatic phase followed by the clinical stage traditionally recognized by neurologists. As clinical trials of potential disease-modifying therapies continue, important developments in the understanding of the disease will improve clinical care now and lead to more effective therapies in the near future.

Associations Between Dietary Patterns and Neuroimaging Markers: A Systematic Review

Dementia is a complex, growing challenge for population health worldwide. Dietary patterns (DPs) may offer an opportunity to beneficially influence cognitive ageing and potentially reduce an individuals’ risk of dementia through diet-related mechanisms. However, previous studies within this area have shown mixed results, which may be partly explained by the lack of sensitivity and accuracy within cognitive testing methods. Novel neuroimaging techniques provide a sensitive method to analyse brain changes preceding cognitive impairment which may have previously remained undetected. The purpose of this systematic review was to elucidate the role of DPs in relation to brain ageing processes, by summarising current prospective and intervention studies. Nine prospective studies met the inclusion criteria for the review, seven evaluated the Mediterranean diet (MeDi), one evaluated the Alternative Healthy Eating Index-2010, and one evaluated a posteriori derived DPs. No intervention studies were eligible for inclusion in this review. There was some evidence of an association between healthy DPs and neuroimaging markers including changes within these markers over time. Consequently, it is plausible that better adherence to such DPs may positively influence brain ageing and neurodegeneration. Future studies may benefit from the use of multi-modal neuroimaging techniques, to further investigate how adherence to a DP influences brain health. The review also highlights the crucial need for further intervention studies within this research area.

Cognitive Test Scores and Progressive Cognitive Decline in the Aberdeen 1921 and 1936 Birth Cohorts

The Aberdeen birth cohorts of 1921 and 1936 (ABC21 and ABC36) were subjected to IQ tests in 1932 or 1947 when they were aged about 11y. They were recruited between 1997–2001 among cognitively healthy community residents and comprehensively phenotyped in a long-term study of brain aging and health up to 2017. Here, we report associations between baseline cognitive test scores and long-term cognitive outcomes. On recruitment, significant sex differences within and between the ABC21 and ABC36 cohorts supported advantages in verbal ability and learning among the ABC36 women that were not significant in ABC21. Comorbid physical disorders were self-reported in both ABC21 and ABC36 but did not contribute to differences in terms of performance in cognitive tests. When used alone without other criteria, cognitive tests scores which fell below the −1.5 SD criterion for tests of progressive matrices, namely verbal learning, digit symbol and block design, did not support the concept that Mild Cognitive Impairment (MCI) is a stable class of acquired loss of function with significant links to the later emergence of a clinical dementia syndrome. This is consistent with many previous reports. Furthermore, because childhood IQ-type data were available, we showed that a lower cognitive performance at about 64 or 78 y than that predicted by IQ at 11 ± 0.5 y did not improve the prediction of progress to MCI or greater cognitive loss. We used binary logistic regression to explore how MCI might contribute to the prediction of later progress to a clinical dementia syndrome. In a fully adjusted model using ABC21 data, we found that non-amnestic MCI, along with factors such as female sex and depressive symptoms, contributed to the prediction of later dementia. A comparable model using ABC36 data did not do so. We propose that (1) MCI criteria restricted to cognitive test scores do not improve the temporal stability of MCI classifications; (2) pathways towards dementia may differ according to age at dementia onset and (3) the concept of MCI may require measures (not captured here) that underly self-reported subjective age-related cognitive decline.

Monthly At-Home Computerized Cognitive Testing to Detect Diminished Practice Effects in Preclinical Alzheimer's Disease

Introduction: We investigated whether monthly assessments of a computerized cognitive composite (C3) could aid in the detection of differences in practice effects (PE) in clinically unimpaired (CU) older adults, and whether diminished PE were associated with Alzheimer's disease (AD) biomarkers and annual cognitive decline. Materials and Methods: N = 114 CU participants (age 77.6 ± 5.0, 61% female, MMSE 29 ± 1.2) from the Harvard Aging Brain Study completed the self-administered C3 monthly, at-home, on an iPad for one year. At baseline, participants underwent in-clinic Preclinical Alzheimer's Cognitive Composite-5 (PACC5) testing, and a subsample (n = 72, age = 77.8 ± 4.9, 59% female, MMSE 29 ± 1.3) had 1-year follow-up in-clinic PACC5 testing available. Participants had undergone PIB-PET imaging (0.99 ± 1.6 years before at-home baseline) and Flortaucipir PET imaging (n = 105, 0.62 ± 1.1 years before at-home baseline). Linear mixed models were used to investigate change over months on the C3 adjusting for age, sex, and years of education, and to extract individual covariate-adjusted slopes over the first 3 months. We investigated the association of 3-month C3 slopes with global amyloid burden and tau deposition in eight predefined regions of interest, and conducted Receiver Operating Characteristic analyses to examine how accurately 3-month C3 slopes could identify individuals that showed >0.10 SD annual decline on the PACC-5. Results: Overall, individuals improved on all C3 measures over 12 months (β = 0.23, 95% CI [0.21-0.25], p < 0.001), but improvement over the first 3 months was greatest (β = 0.68, 95% CI [0.59-0.77], p < 0.001), suggesting stronger PE over initial repeated exposures. However, lower PE over 3 months were associated with more global amyloid burden (r = -0.20, 95% CI [-0.38 - -0.01], p = 0.049) and tau deposition in the entorhinal cortex (r = -0.38, 95% CI [-0.54 - -0.19], p < 0.001) and inferior-temporal lobe (r = -0.23, 95% CI [-0.41 - -0.02], p = 0.03). 3-month C3 slopes exhibited good discriminative ability to identify PACC-5 decliners (AUC 0.91, 95% CI [0.84-0.98]), which was better than baseline C3 (p < 0.001) and baseline PACC-5 scores (p = 0.02). Conclusion: While PE are commonly observed among CU adults, diminished PE over monthly cognitive testing are associated with greater AD biomarker burden and cognitive decline. Our findings imply that unsupervised computerized testing using monthly retest paradigms can provide rapid detection of diminished PE indicative of future cognitive decline in preclinical AD.

Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer's Disease

Background:

In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status.

Objective:

It remains unexamined how complex patterns of the test performances can be utilized to have specific predictive meaning when the machine learning approach is applied.

Methods:

In this study, the neuropsychological battery (CERAD-K) and assessment of functioning level (Clinical Dementia Rating scale and Instrumental Activities of Daily Living) were administered to 2,642 older adults with no impairment (n = 285), mild cognitive impairment (n = 1,057), and Alzheimer’s disease (n = 1,300). Predictive accuracy on functional impairment level with the linear models of the single total score or multiple subtest scores (Model 1, 2) and support vector regression with low or high complexity (Model 3, 4) were compared across different sample sizes.

Results:

The linear models (Model 1, 2) showed superior performance with relatively smaller sample size, while nonlinear models with low and high complexity (Model 3, 4) showed an improved accuracy with a larger dataset. Unlike linear models, the nonlinear models showed a gradual increase in the predictive accuracy with a larger sample size (n > 500), especially when the model training is allowed to exploit complex patterns of the dataset.

Conclusion:

Our finding suggests that nonlinear models can predict levels of functional impairment with a sufficient dataset. The summary index of the neuropsychological battery can be augmented for specific purposes, especially in estimating the functional status of dementia.

Methylglyoxal affects cognitive behaviour and modulates RAGE and Presenilin-1 expression in hippocampus of aged mice

Methylglyoxal (MG), a potent glycotoxin that can be found in the diet, is one of the main precursors of Advanced glycation end products (AGEs). It is well known that modifications in lifestyle such as nutritional interventions can be of great value for preventing brain deterioration. This study aimed to evaluate in vivo how an oral MG treatment, that mimics a high MG dietary intake, could affect brain health. From our results, we demonstrated that MG administration affected working memory, and induced neuroinflammation and oxidative stress by modulating the Receptor for Advanced glycation end products (RAGE). The gene and protein expressions of RAGE were increased in the hippocampus of MG mice, an area where the activity of glyoxalase 1, one of the main enzymes involved in MG detoxification, was found reduced. Furthermore, at hippocampus level, MG mice showed increased expression of proinflammatory cytokines and increased activities of NADPH oxidase and catalase. MG administration also increased the gene and protein expressions of Presenilin-1, a subunit of the gamma-secretase protein complex linked to Alzheimer's disease. These findings suggest that high MG oral intake induces alteration directly in the brain and might establish an environment predisposing to AD-like pathological conditions.

A Comparison of Cross-Sectional and Longitudinal Methods of Defining Objective Subtle Cognitive Decline in Preclinical Alzheimer's Disease Based on Cogstate One Card Learning Accuracy Performance

BACKGROUND

Longitudinal, but not cross-sectional, cognitive testing is one option proposed to define transitional cognitive decline for individuals on the Alzheimer's disease continuum.

OBJECTIVE

Compare diagnostic accuracy of cross-sectional subtle objective cognitive impairment (sOBJ) and longitudinal objective decline (ΔOBJ) over 30 months for identifying 1) cognitively unimpaired participants with preclinical Alzheimer's disease defined by elevated brain amyloid and tau (A+T+) and 2) incident mild cognitive impairment (MCI) based on Cogstate One Card Learning (OCL) accuracy performance.

METHODS

Mayo Clinic Study of Aging cognitively unimpaired participants aged 50 + with amyloid and tau PET scans (n = 311) comprised the biomarker-defined sample. A case-control sample of participants aged 65 + remaining cognitively unimpaired for at least 30 months included 64 who subsequently developed MCI (incident MCI cases) and 184 controls, risk-set matched by age, sex, education, and visit number. sOBJ was assessed by OCL z-scores. ΔOBJ was assessed using within subjects' standard deviation and annualized change from linear regression or linear mixed effects (LME) models. Concordance measures Area Under the ROC Curve (AUC) or C-statistic and odds ratios (OR) from conditional logistic regression models were derived. sOBJ and ΔOBJ were modeled jointly to compare methods.

RESULTS

sOBJ and ΔOBJ-LME methods differentiated A+T+ from A-T- (AUC = 0.64, 0.69) and controls from incident MCI (C-statistic = 0.59, 0.69) better than chance; other ΔOBJ methods did not. ΔOBJ-LME improved prediction of future MCI over baseline sOBJ (p = 0.003) but not over 30-month sOBJ (p = 0.09).

CONCLUSION

Longitudinal decline did not offer substantial benefit over cross-sectional assessment in detecting preclinical Alzheimer's disease or incident MCI.