Biomarkers in Alzheimer's disease

Relationship of Alberta Stroke Program Early CT Score (ASPECTS) with the outcome of ischemic stroke and the neurocognitive stroke biomarkers

Background

Reliable and acceptable biomarkers are needed to anticipate the outcome and cognitive impairment following ischemic stroke. The goal of this research is to examine the association of ASPECTS with cognitive decline, biomarkers of stroke, and acute ischemic stroke outcomes. This study included 120 patients with ischemic stroke in the middle cerebral artery region. The initial NIHSS, non-contrast CT brain assessed by ASPECTS, and the biomarkers of cognitive decline such as ESR, CRP, S100B, MMP9, and glutamate were investigated. The Montreal Cognitive Assessment and modified Rankin scale (mRS) were evaluated after 3 months. Correlations between ASPECTS, MoCA, biomarkers of cognitive impairment, and mRS were done by Spearman correlation.

Results

The incidence of cognitive impairment in our patients was 25.8%. Stroke biomarkers (ESR, CRP, S100B, MMP9, and glutamate) were significantly increased in cognitively disabled individuals with significantly lower mean MoCA scores than in cognitively intact patients. There was a strong direct correlation linking the initial ASPECTS and total MoCA test score after 3 months follow-up. Cases with unfavorable outcomes were older, more incidence of hypertension, and had higher average initial NIHSS (P < 0.05). While the average ASPECTS scores for the favorable outcome group of patients were significantly higher and there was a significant negative correlation between the initial ASPECTS and modified Rankin Scale score.

Conclusions

ASPECTS is a reliable scale to identify the extent of acute ischemic injury and could participate in assessing the outcome. ASPECTS and particular neurocognitive stroke biomarkers will enable the early detection of post-stroke cognitive impairment.

Trial registration Registration of Clinical Trial Research: ClinicalTrials.gov ID: NCT04235920

Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

In Alzheimer's disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer's disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction.

Early- and late-onset Alzheimer disease: Are they the same entity?

Early-onset Alzheimer disease (EOAD), which presents in patients younger than 65 years, has frequently been described as having different features from those of late-onset Alzheimer disease (LOAD). This review analyses the most recent studies comparing the clinical presentation and neuropsychological, neuropathological, genetic, and neuroimaging findings of both types in order to determine whether EOAD and LOAD are different entities or distinct forms of the same entity. We observed consistent differences between clinical findings in EOAD and in LOAD. Fundamentally, the onset of EOAD is more likely to be marked by atypical symptoms, and cognitive assessments point to poorer executive and visuospatial functioning and praxis with less marked memory impairment. Alzheimer-type features will be more dense and widespread in neuropathology studies, with structural and functional neuroimaging showing greater and more diffuse atrophy extending to neocortical areas (especially the precuneus). In conclusion, available evidence suggests that EOAD and LOAD are 2 different forms of a single entity. LOAD is likely to be influenced by ageing-related processes.

Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease

Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment.

Amyloid imaging in Alzheimer's disease: a literature review

Therapies targeting amyloid-β peptide currently represent approximately 50% of drugs now being developed for Alzheimer's disease. Some, including active and passive anti-Aβ immunotherapy, directly target the amyloid plaques. The new amyloid tracers are increasingly being included in the proposed updated diagnostic criteria, and may allow earlier diagnosis. Those targeting amyloid-β peptide allow identification of amyloid plaques in vivo. We need to gain insight into all aspects of their application. As florbetapir (Amyvid™) and flutemetamol (Vizamyl™) have received marketing authorization, clinicians require deeper knowledge to be rationally used in diagnosis. In this paper, we review both completed and ongoing observational, longitudinal and interventional studies of these tracers, our main objective being to show the performance of the four most commonly used tracers and their validation.

A behavioral task predicts conversion to mild cognitive impairment and Alzheimer's disease

BACKGROUND/RATIONALE

Currently, we cannot reliably differentiate individuals at risk of cognitive decline, for example, mild cognitive impairment (MCI), Alzheimer's disease (AD), from those individuals who are not at risk.

METHODS

A total of 32 participants with MCI and 60 control (CON) participants were tested on an innovative, sensitive behavioral assay, the visual paired comparison (VPC) task using infrared eye tracking. The participants were followed for 3 years after testing.

RESULTS

Scores on the VPC task predicted, up to 3 years prior to a change in clinical diagnosis, those patients with MCI who would and who would not progress to AD and CON participants who would and would not progress to MCI.

CONCLUSIONS

The present findings show that the VPC task can predict impending cognitive decline. To our knowledge, this is the first behavioral task that can identify CON participants who will develop MCI or patients with MCI who will develop AD within the next few years.

The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry

The National Institute of Mental Health strategic plan for advancing psychiatric neuroscience calls for an acceleration of discovery and the delineation of developmental trajectories for risk and resilience across the lifespan. To attain these objectives, sufficiently powered datasets with broad and deep phenotypic characterization, state-of-the-art neuroimaging, and genetic samples must be generated and made openly available to the scientific community. The enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) is a response to this need. NKI-RS is an ongoing, institutionally centered endeavor aimed at creating a large-scale (N > 1000), deeply phenotyped, community-ascertained, lifespan sample (ages 6-85 years old) with advanced neuroimaging and genetics. These data will be publically shared, openly, and prospectively (i.e., on a weekly basis). Herein, we describe the conceptual basis of the NKI-RS, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment. Additionally, we describe our process for sharing the data with the scientific community while protecting participant confidentiality, maintaining an adequate database, and certifying data integrity. The pilot phase of the NKI-RS, including challenges in recruiting, characterizing, imaging, and sharing data, is discussed while also explaining how this experience informed the final design of the enhanced NKI-RS. It is our hope that familiarity with the conceptual underpinnings of the enhanced NKI-RS will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.

CSF biomarkers in different phenotypes of Parkinson disease

CSF biomarker studies were performed in 6 patients each with tremor-dominant (TD) and non-tremor-dominant (NT) Parkinson disease (PD) patients, 27 Alzheimer disease (AD) and 17 age-matched controls. In both NT-PD and AD patients total tau levels and the cortex tau/Aβ-42 were significantly increased compared to both TD-PD patients and controls (p < 0.01). These data in a small cohort confirm previous studies, corroborating the opinion that CSF levels of tau protein and the index total-tau/Aβ-42 may be potential markers of the severity of neurodegeneration in PD.