A blood-based signature of cerebrospinal fluid Aβ1-42 status

Novel plasma protein biomarkers: A time-dependent predictive model for Alzheimer's disease

BACKGROUND

The accurate prediction of Alzheimer's disease (AD) is crucial for the efficient management of its progression. The objective of this research was to construct a new risk predictive model utilizing novel plasma protein biomarkers for predicting AD incidence in the future and analyze their potential biological correlation with AD incidence.

METHODS

A cohort of 440 participants aged 60 years and older from the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal cohort was utilized. The baseline plasma proteomics data was employed to conduct Cox regression, LASSO regression, and cross-validation to identify plasma protein signatures predictive of AD risk. Subsequently, a multivariable Cox proportional hazards model based on these signatures was constructed. The performance of the risk prediction model was evaluated using time-dependent receiver operating characteristic (t-ROC) curves and Kaplan-Meier curves. Additionally, we analyzed the correlations between protein signature expression in plasma and predicted AD risk, the time of AD onset, the expression of protein signatures in cerebrospinal fluid (CSF), the expression of CSF and plasma biomarkers, and APOE ε4 genotypes. Colocalization and Mendelian randomization analyses was conducted to investigate the association between protein features and AD risk. GEO database was utilized to analyze the differential expression of protein features in the blood and brain of AD patients.

RESULTS

We identified seven protein signatures (APOE, CGA, CRP, CCL26, CCL20, NRCAM, and PYY) that independently predicted AD incidence in the future. The risk prediction model demonstrated area under the ROC curve (AUC) values of 0.77, 0.76, and 0.77 for predicting AD incidence at 4, 6, and 8 years, respectively. Furthermore, the model remained stable in the range of the 3rd to the 12th year (ROC ≥ 0.74). The low-risk group, as defined by the model, exhibited a significantly later AD onset compared to the high-risk group (P < 0.0001). Moreover, all protein signatures exhibited significant correlations with AD risk (P < 0.001) and the time of AD onset (P < 0.01). There was no strong correlation between the protein expression levels in plasma and CSF, as well as AD CSF biomarkers. APOE, CGA, and CRP exhibited significantly lower expression levels in APOE ε4 positive individuals (P < 0.05). Additionally, colocalization analysis reveals a significant association between AD and SNP loci in APOE. Mendelian randomization analysis shows a negative correlation between NRCAM and AD risk. Transcriptomic analysis indicates a significant downregulation of NRCAM and PYY in the peripheral blood of AD patients (P < 0.01), while APOE, CGA, and NRCAM are significantly downregulated in the brains of AD patients (P < 0.0001).

CONCLUSION

Our research has successfully identified protein signatures in plasma as potential risk biomarkers that can independently predict AD onset in the future. Notably, this risk prediction model has demonstrated commendable predictive performance and stability over time. These findings underscore the promising utility of plasma protein signatures in dynamically predicting the risk of AD, thereby facilitating early screening and intervention strategies.

Association of Plasma Aβ42/Aβ40 Ratio and Late-Onset Epilepsy: Results From the Atherosclerosis Risk in Communities Study

BACKGROUND AND OBJECTIVES

The objective of this study was to determine the relationship between plasma β-amyloid (Aβ), specifically the ratio of 2 Aβ peptides (the Aβ42/Aβ40 ratio, which correlates with increased accumulation of Aβ in the CNS), and late-onset epilepsy (LOE).

METHODS

We used Medicare fee-for-service claims codes from 1991 to 2018 to identify cases of LOE among 1,424 Black and White men and women enrolled in the Atherosclerosis Risk in Communities (ARIC) study cohort. The Aβ42/Aβ40 ratio was calculated from plasma samples collected from ARIC participants in 1993-1995 (age 50-71 years) and 2011-2013 (age 67-90 years). We used survival analysis accounting for the competing risk of death to determine the relationship between late-life plasma Aβ42/Aβ40, and its change from midlife to late life, and the subsequent development of epilepsy. We adjusted for demographics, the apolipoprotein e4 genotype, and comorbidities, including stroke, dementia, and head injury. A low plasma ratio of 2 Aβ peptides, the Aβ42/Aβ40 ratio, correlates with low CSF Aβ42/Aβ40 and with increased accumulation of Aβ in the CNS.

RESULTS

Decrease in plasma Aβ42/Aβ40 ratio from midlife to late life, but not an isolated measurement of Aβ42/Aβ40, was associated with development of epilepsy in later life. For every 50% reduction in Aβ42/Aβ40, there was a 2-fold increase in risk of epilepsy (adjusted subhazard ratio 2.30, 95% CI 1.27-4.17).

DISCUSSION

A reduction in plasma Aβ42/Aβ40 is associated with an increased risk of subsequent epilepsy. Our observations provide a further validation of the link between Aβ, hyperexcitable states, and LOE.

Predicting AT(N) pathologies in Alzheimer's disease from blood-based proteomic data using neural networks

BACKGROUND AND OBJECTIVE

Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD.

METHODS

We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis.

RESULTS

Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway.

CONCLUSION

Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.

Early detection of Alzheimer's disease using neuropsychological tests: a predict-diagnose approach using neural networks

Alzheimer’s disease (AD) is a slowly progressing disease for which there is no known therapeutic cure at present. Ongoing research around the world is actively engaged in the quest for identifying markers that can help predict the future cognitive state of individuals so that measures can be taken to prevent the onset or arrest the progression of the disease. Researchers are interested in both biological and neuropsychological markers that can serve as good predictors of the future cognitive state of individuals. The goal of this study is to identify non-invasive, inexpensive markers and develop neural network models that learn the relationship between those markers and the future cognitive state. To that end, we use the renowned Alzheimer’s Disease Neuroimaging Initiative (ADNI) data for a handful of neuropsychological tests to train Recurrent Neural Network (RNN) models to predict future neuropsychological test results and Multi-Level Perceptron (MLP) models to diagnose the future cognitive states of trial participants based on those predicted results. The results demonstrate that the predicted cognitive states match the actual cognitive states of ADNI test subjects with a high level of accuracy. Therefore, this novel two-step technique can serve as an effective tool for the prediction of Alzheimer’s disease progression. The reliance of the results on inexpensive, non-invasive tests implies that this technique can be used in countries around the world including those with limited financial resources.

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

METHODS

We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

RESULTS

Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

DISCUSSION

ADNI has had a profound impact in improving clinical trials for AD.

1H-Pyrazolo[3,4-b]pyridines: Synthesis and Biomedical Applications

Pyrazolo[3,4-b]pyridines are a group of heterocyclic compounds presenting two possible tautomeric forms: the 1H- and 2H-isomers. More than 300,000 1H-pyrazolo[3,4-b]pyridines have been described which are included in more than 5500 references (2400 patents) up to date. This review will cover the analysis of the diversity of the substituents present at positions N1, C3, C4, C5, and C6, the synthetic methods used for their synthesis, starting from both a preformed pyrazole or pyridine, and the biomedical applications of such compounds.

Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry

INTRODUCTION

This study investigated the extent to which subjective and objective data from an online registry can be analyzed using machine learning methodologies to predict the current brain amyloid beta (Aβ) status of registry participants.

METHODS

We developed and optimized machine learning models using data from up to 664 registry participants. Models were assessed on their ability to predict Aβ positivity using the results of positron emission tomography as ground truth.

RESULTS

Study partner-assessed Everyday Cognition score was preferentially selected for inclusion in the models by a feature selection algorithm during optimization.

DISCUSSION

Our results suggest that inclusion of study partner assessments would increase the ability of machine learning models to predict Aβ positivity.

Early Identification of Alzheimer's Disease in Mouse Models: Application of Deep Neural Network Algorithm to Cognitive Behavioral Parameters

Alzheimer's disease (AD) is a worldwide burden. Diagnosis is complicated by the fact that AD is asymptomatic at an early stage. Studies using AD-modeled animals offer important and useful insights. Here, we classified mice with a high risk of AD at a preclinical stage by using only their behaviors. Wild-type and knock-in AD-modeled (App NL-G-F/NL-G-F ) mice were raised, and their cognitive behaviors were assessed in an automated monitoring system. The classification utilized a machine learning method, i.e., a deep neural network, together with optimized stepwise feature selection and cross-validation. The AD risk could be identified on the basis of compulsive and learning behaviors (89.3% ± 9.8% accuracy) shown by AD-modeled mice in the early age (i.e., 8-12 months old) when the AD symptomatic cognitions were relatively underdeveloped. This finding reveals the advantage of machine learning in unveiling the importance of compulsive and learning behaviors for early AD diagnosis in mice.

The search for a convenient procedure to detect one of the earliest signs of Alzheimer's disease: A systematic review of the prediction of brain amyloid status

INTRODUCTION

Convenient, cost-effective tests for amyloid beta (Aβ) are needed to identify those at higher risk for developing Alzheimer's disease (AD). This systematic review evaluates recent models that predict dichotomous Aβ. (PROSPERO: CRD42020144734).

METHODS

We searched Embase and identified 73 studies from 29,581 for review. We assessed study quality using established tools, extracted information, and reported results narratively.

RESULTS

We identified few high-quality studies due to concerns about Aβ determination and analytical issues. The most promising convenient, inexpensive classifiers consist of age, apolipoprotein E genotype, cognitive measures, and/or plasma Aβ. Plasma Aβ may be sufficient if pre-analytical variables are standardized and scalable assays developed. Some models lowered costs associated with clinical trial recruitment or clinical screening.

DISCUSSION

Conclusions about models are difficult due to study heterogeneity and quality. Promising prediction models used demographic, cognitive/neuropsychological, imaging, and plasma Aβ measures. Further studies using standardized Aβ determination, and improved model validation are required.

Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers

In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer's disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer's disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer's Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80-0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69-0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06-0.14 units of area under curve for cognitively unimpaired, and by 0.21-0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04-0.11 units of area under curve for cognitively unimpaired and 0.05-0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80-0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.

A Comparative Study on the Predictive Value of Different Resting-State Functional Magnetic Resonance Imaging Parameters in Preclinical Alzheimer's Disease

Objective: Diverse resting-state functional magnetic resonance imaging (rs-fMRI) studies showed that rs-fMRI might be able to reflect the earliest detrimental effect of cerebral beta-amyloid (Aβ) pathology. However, no previous studies specifically compared the predictive value of different rs-fMRI parameters in preclinical AD. Methods: A total of 106 cognitively normal adults (Aβ+ group = 66 and Aβ- group = 40) were included. Three different rs-fMRI parameter maps including functional connectivity (FC), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) were calculated. Receiver operating characteristic (ROC) curve analyses were utilized to compare classification performance of the three rs-fMRI parameters. Results: FC maps showed the best classifying performance in ROC curve analysis (AUC, 0.915, p < 0.001). Good but weaker performance was achieved by using ReHo maps (AUC, 0.836, p < 0.001) and fALFF maps (AUC, 0.804, p < 0.001). The brain regions showing the greatest discriminative power included the left angular gyrus for FC, left anterior cingulate for ReHo, and left middle frontal gyrus for fALFF. However, among the three measurements, ROI-based FC was the only measure showing group difference in voxel-wise analysis. Conclusion: Our results strengthen the idea that rs-fMRI might be sensitive to earlier changes in spontaneous brain activity and FC in response to cerebral Aβ retention. However, further longitudinal studies with larger sample sizes are needed to confirm their utility in predicting the risk of AD.

Diverse Applications of Artificial Intelligence in Neuroradiology

Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."

A Robust Blood-based Signature of Cerebrospinal Fluid Aβ42 Status

Early detection of Alzheimer's disease (AD) is of vital importance in the development of disease-modifying therapies. This necessitates the use of early pathological indicators of the disease such as amyloid abnormality to identify individuals at early disease stages where intervention is likely to be most effective. Recent evidence suggests that cerebrospinal fluid (CSF) amyloid β_1-42 (Aβ₄₂) level may indicate AD risk earlier compared to amyloid positron emission tomography (PET). However, the method of collecting CSF is invasive. Blood-based biomarkers indicative of CSF Aβ₄₂ status may remedy this limitation as blood collection is minimally invasive and inexpensive. In this study, we show that APOE4 genotype and blood markers comprising EOT3, APOC1, CGA, and Aβ₄₂ robustly predict CSF Aβ₄₂ with high classification performance (0.84 AUC, 0.82 sensitivity, 0.62 specificity, 0.81 PPV and 0.64 NPV) using machine learning approach. Due to the method employed in the biomarker search, the identified biomarker signature maintained high performance in more than a single machine learning algorithm, indicating potential to generalize well. A minimally invasive and cost-effective solution to detecting amyloid abnormality such as proposed in this study may be used as a first step in a multi-stage diagnostic workup to facilitate enrichment of clinical trials and population-based screening.

C-reactive protein as a predictor of mild cognitive impairment conversion into Alzheimer's disease dementia

BACKGROUND AND AIMS

Increasing evidence suggests that inflammation plays an important role in brain aging and neurodegeneration. Pathological studies demonstrate the presence of C-reactive protein (CRP) in the senile plaques and neurofibrillary tangles in Alzheimer's disease (AD) brain tissue suggesting that CRP may play a role in its neuropathological processes. Some findings suggest that midlife elevations of serum CRP are a risk factor for AD. However, others found lower CRP levels in mild or moderate AD than in controls, suggesting that CRP levels could be different in different stages of disease. We aimed to assess the role of CRP as a predictor of Mild cognitive impairment (MCI) conversion into AD dementia.

METHODS

We retrospectively reviewed the cohort of MCI patients followed at the Dementia Clinic, Neurology Department of University Hospital of Coimbra. We collected demographical, neuropsychological, genetic and laboratorial variables (including serum CRP measurements at the time of baseline laboratory tests). A Cox regression model was performed adjusted for the collected variables preconsidered to be predictors of dementia and the variable being studied (CRP) to assess for independent predictors of conversion.

RESULTS

We included 130 patients, 58.5% female, with a mean age of onset of 65.5 ± 9.1 years and age at first assessment of 69.3 ± 8.5 years. The mean CRP was 0.33 ± 0.58 mg/dl. At follow-up (mean, 36.9 ± 27.0 months) 42.3% of MCI patients converted to dementia. Lower CSF Aβ42 (HR = 0.999, 95%CI = [0.997, 1.000], p = 0.015), lower MMSE score (HR = 0.864, 95%CI = [0.510, 1.595], p = 0.008) and lower CRP quartile (HR = 0.597, 95%CI = [0.435, 0.819], p = 0.001) were independent predictors of conversion.

CONCLUSION

CRP may add information of risk of conversion in MCI patients. Patients with lower CRP levels appear to have a more rapid conversion to AD dementia.

Artificial Intelligence for Clinical Trial Design

Clinical trials consume the latter half of the 10 to 15 year, 1.5-2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates.

Blood-brain barrier and innate immunity in the pathogenesis of Alzheimer's disease

The pathogenesis of Alzheimer's disease (AD) is only partly understood. This is the probable reason why significant efforts to treat or prevent AD have been unsuccessful. In fact, as of April 2019, there have been 2094 studies registered for AD on the clinicaltrials.gov U.S. National Library of Science web page, of which only a few are still ongoing. In AD, abnormal accumulation of amyloid and tau proteins in the brain are thought to begin 10-20 years before the onset of overt symptoms, suggesting that interventions designed to prevent pathological amyloid and tau accumulation may be more effective than attempting to reverse a pathology once it is established. However, to be successful, such early interventions need to be selectively administered to individuals who will likely develop the disease long before the symptoms occur. Therefore, it is critical to identify early biomarkers that are strongly predictive of AD. Currently, patients are diagnosed on the basis of a variety of clinical scales, neuropsychological tests, imaging and laboratory modalities, but definitive diagnosis can be made only by postmortem assessment of underlying neuropathology. People suffering from AD thus may be misdiagnosed clinically with other primary causes of dementia, and vice versa, thereby also reducing the power of clinical trials. The amyloid cascade hypothesis fits well for the familial cases of AD with known mutations, but is not sufficient to explain sporadic, late-onset AD (LOAD) that accounts for over 95% of all cases. Since the earliest descriptions of AD there have been neuropathological features described other than amyloid plaques (AP) and neurofibrillary tangles (NFT), most notably gliosis and neuroinflammation. However, it is only recently that genetic and experimental studies have implicated microglial dysfunction as a causal factor for AD, as opposed to a merely biological response of its accumulation around AP. Additionally, many studies have suggested the importance of changes in blood-brain barrier (BBB) permeability in the pathogenesis of AD. Here we suggest how these less investigated aspects of the disease that have gained increased attention in recent years may contribute mechanistically to the development of lesions and symptoms of AD.