Identification of a Simple and Novel Cut-Point Based Cerebrospinal Fluid and MRI Signature for Predicting Alzheimer's Disease Progression that Reinforces the 2018 NIA-AA Research Framework

Plasma pTau181 enhances the prediction of future clinical decline in amyloid-positive mild cognitive impairment

Abstract

Plasma pTau181, a marker of amyloid and tau burden, was evaluated as a prognostic predictor of clinical decline and Alzheimer's disease (AD) progression of amyloid-positive (Aβ+) patients with mild cognitive impairment (MCI). The training cohort for constructing the Bayesian prediction models comprised 135 Aβ+ MCI clinical trial placebo subjects. Performance was evaluated in two validation cohorts. An 18-month ≥1 increase in the Clinical Dementia Rating Sum of Boxes was the clinical decline criterion. Baseline plasma pTau181 concentration matched clinical assessments' prediction performance. Adding pTau181 to clinical assessments significantly improved the prediction of an 18-month clinical decline and the 36-month progression from Aβ+ MCI to AD. The area under the receiver operating characteristic curve for the latter increased from 71.8% to 79%, and the hazard ratio for time-to-progression improved from 2.26 to 3.11 (p < 0.0001). Baseline plasma pTau181 has the potential for identifying Aβ+ MCI subjects with faster clinical decline over time.

Highlights

This study assessed pTau181 as a prognostic predictor of 18-month clinical decline and extended progression to Alzheimer's disease (AD) in amyloid-positive patients with mild cognitive impairment (Aβ+ MCI).The research findings underscore the promise of baseline plasma pTau181 as a screening tool for identifying Aβ+ MCI individuals with accelerated clinical decline within a standard 18-month clinical trial period. The predictive accuracy is notably enhanced when combined with clinical assessments.Similar positive outcomes were noted in forecasting the extended progression of Aβ+ MCI subjects to AD.

Predicting mild cognitive impairment in older adults: A machine learning analysis of the Alzheimer's Disease Neuroimaging Initiative

AIM

Mild cognitive impairment (MCI) in older adults is potentially devastating, but an accurate prediction model is still lacking. We hypothesized that neuropsychological tests and MRI-related markers could predict the onset of MCI early.

METHODS

We analyzed data from 306 older adults who were cognitive normal (CN) attending the Alzheimer's Disease Neuroimaging Initiative sequentially (474 pairs of visits) within 3 years. There were 231 pairs of MCI conversion (CN to MCI), and 242 pairs of CN maintenance (CN to CN). Variables on demographic, neuropsychological tests, genetic, and MRI-related markers were collected. Machine learning was used to construct MCI prediction models, comparing the area under the receiver operating characteristic curve (AUC) as the primary metric of performance. Important predictors were ranked for the optimal model.

RESULTS

The baseline age of the study sample was 74.8 years old. The best-performing model (gradient boosting decision tree) with 13 variables predicted MCI with an AUC of 0.819, and the rank of variable importance showed that intracranial volume, hippocampal volume, and score from task 4 (word recognition) of the Alzheimer's Disease Assessment Scale were important predictors of MCI.

CONCLUSIONS

With the help of machine learning, fewer neuropsychological tests and MRI-related markers are required to accurately predict MCI within 3 years, thereby facilitating targeted intervention. Geriatr Gerontol Int 2024; 24: 96-101.

Predicting clinical progression trajectories of early Alzheimer's disease patients

BACKGROUND

Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring.

METHODS

Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures.

RESULTS

The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%.

DISCUSSION

Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.

Intracerebral Nanoparticle Transport Facilitated by Alzheimer Pathology and Age

Nanoparticles have emerged as potential transporters of drugs targeting Alzheimer's disease (AD), but their design should consider the blood-brain barrier (BBB) integrity and neuroinflammation of the AD brain. This study presents that aging is a significant factor for the brain localization and retention of nanoparticles, which we engineered to bind with reactive astrocytes and activated microglia. We assembled 200 nm-diameter particles using a block copolymer of poly(lactic-co-glycolic acid) (PLGA) and CD44-binding hyaluronic acid (HA). The resulting PLGA-b-HA nanoparticles displayed increased binding to CD44-expressing reactive astrocytes and activated microglia. Upon intravascular injection, nanoparticles were localized to the hippocampi of both APP/PS1 AD model mice and their control littermates at 13-16 months of age due to enhanced transvascular transport through the leaky BBB. No particles were found in the hippocampi of young adult mice. These findings demonstrate the brain localization of nanoparticles due to aging-induced BBB breakdown regardless of AD pathology.

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease

Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.

Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals

BACKGROUND

Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations.

METHODS

Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance.

RESULTS

The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors.

CONCLUSION

Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.

CSF peptides from VGF and other markers enhance prediction of MCI to AD progression using the ATN framework

The amyloid beta, tau, neurodegenerative markers framework has been proposed to serve as a system to classify and combine biomarkers for Alzheimer's Disease (AD). Although cerebrospinal (CSF) fluid AT (amyloid beta and tau)-based biomarkers have a well-established track record to distinguish AD from control subjects and to predict conversion from mild cognitive impairment (MCI) to AD, there is not an established non-tau based neurodegenerative ("N") marker from CSF. Here, we examine the ability of several candidate peptides in the CSF to serve as "N" markers to both classify disease state and predict MCI to AD conversion. We observed that although many putative N markers involved in synaptic processing and neuroinflammation were able to, when examined in isolation, distinguish MCI converters from non-converters, a derivative from VGF, when combined with AT markers, most strongly enhanced prediction of MCI to AD conversion. Low CSF VGF levels were also predictive of MCI to dementia conversion in the setting of normal AT markers, suggesting that it may serve as a very early predictor of dementia conversion. Other markers derived from neuronal pentraxin 2, GAP-43 and a 14-3-3 protein were also able to enhance MCI to AD prediction when used as a marker of neurodegeneration, but VGF had the highest predictive capacity. Thus, we propose that low levels of VGF in CSF may serve as "N" in the amyloid beta, tau, neurodegenerative markers framework to enhance the prediction of MCI to AD conversion.

Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study

Objective

To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI).

Materials and Methods

A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer’s disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test.

Results

The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer’s continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer’s disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD.

Conclusion

We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.

Developing the ATX(N) classification for use across the Alzheimer disease continuum

Breakthroughs in the development of highly accurate fluid and neuroimaging biomarkers have catalysed the conceptual transformation of Alzheimer disease (AD) from the traditional clinical symptom-based definition to a clinical-biological construct along a temporal continuum. The AT(N) system is a symptom-agnostic classification scheme that categorizes individuals using biomarkers that chart core AD pathophysiological features, namely the amyloid-β (Aβ) pathway (A), tau-mediated pathophysiology (T) and neurodegeneration (N). This biomarker matrix is now expanding towards an ATX(N) system, where X represents novel candidate biomarkers for additional pathophysiological mechanisms such as neuroimmune dysregulation, synaptic dysfunction and blood-brain barrier alterations. In this Perspective, we describe the conceptual framework and clinical importance of the existing AT(N) system and the evolving ATX(N) system. We provide a state-of-the-art summary of the potential contexts of use of these systems in AD clinical trials and future clinical practice. We also discuss current challenges related to the validation, standardization and qualification process and provide an outlook on the real-world application of the AT(N) system.

An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer's disease

BACKGROUND

Multiple pathophysiological processes have been described in Alzheimer's disease (AD). Their inter-individual variations, complex interrelations, and relevance for clinical manifestation and disease progression remain poorly understood. We hypothesize that specific molecular patterns indicating both known and yet unidentified pathway alterations are associated with distinct aspects of AD pathology.

METHODS

We performed multi-level cerebrospinal fluid (CSF) omics in a well-characterized cohort of older adults with normal cognition, mild cognitive impairment, and mild dementia. Proteomics, metabolomics, lipidomics, one-carbon metabolism, and neuroinflammation related molecules were analyzed at single-omic level with correlation and regression approaches. Multi-omics factor analysis was used to integrate all biological levels. Identified analytes were used to construct best predictive models of the presence of AD pathology and of cognitive decline with multifactorial regression analysis. Pathway enrichment analysis identified pathway alterations in AD.

RESULTS

Multi-omics integration identified five major dimensions of heterogeneity explaining the variance within the cohort and differentially associated with AD. Further analysis exposed multiple interactions between single 'omics modalities and distinct multi-omics molecular signatures differentially related to amyloid pathology, neuronal injury, and tau hyperphosphorylation. Enrichment pathway analysis revealed overrepresentation of the hemostasis, immune response, and extracellular matrix signaling pathways in association with AD. Finally, combinations of four molecules improved prediction of both AD (protein 14-3-3 zeta/delta, clusterin, interleukin-15, and transgelin-2) and cognitive decline (protein 14-3-3 zeta/delta, clusterin, cholesteryl ester 27:1 16:0 and monocyte chemoattractant protein-1).

CONCLUSIONS

Applying an integrative multi-omics approach we report novel molecular and pathways alterations associated with AD pathology. These findings are relevant for the development of personalized diagnosis and treatment approaches in AD.

Sectoral segmentation of retinal amyloid imaging in subjects with cognitive decline

INTRODUCTION

Despite advances in imaging retinal amyloidosis, a quantitative and topographical investigation of retinal amyloid beta burden in patients with cognitive decline has never been reported.

METHODS

We used the specific amyloid-binding fluorophore curcumin and laser ophthalmoscopy to assess retinal amyloid imaging (RAI) in 34 patients with cognitive decline. We automatically quantified retinal amyloid count (RAC) and area in the superotemporal retinal sub-regions and performed correlation analyses with cognitive and brain volumetric parameters.

RESULTS

RAC significantly and inversely correlated with hippocampal volume (HV; r = -0.39, P = .04). The proximal mid-periphery (PMP) RAC and RA areas were significantly greater in patients with Montreal Cognitive Assessment (MOCA) score < 26 (P = .01; Cohen d = 0.83 and 0.81, respectively). PMP showed significantly more RAC and area in subjects with amnestic mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared to cognitively normal (P = .04; Cohen d = 0.83).

CONCLUSION

Quantitative RAI is a feasible technique and PMP RAC may predict HV. Future larger studies should determine RAI's potential as a biomarker of early AD.

VGF in Cerebrospinal Fluid Combined With Conventional Biomarkers Enhances Prediction of Conversion From MCI to AD

BACKGROUND

Previous work has suggested that the brain and cerebrospinal fluid (CSF) levels of a neural protein involved in synaptic transmission, VGF (a noninitialism), may be altered in mild cognitive impairment (MCI) and Alzheimer Disease (AD). The objective of the current work is to examine the potential of CSF levels of a peptide derived from VGF to predict conversion from MCI to AD.

MATERIALS AND METHODS

Using multivariate analytical approaches, the performance of the conventional biomarkers (CSF Aβ1-42 and phosphorylated tau +/- hippocampal volume) was compared with the same biomarkers combined with CSF VGF peptide levels in a large publicly available data set from human subjects.

RESULTS

It was observed that VGF peptides are lowered in CSF of patients with AD compared with controls and that combinations of CSF Aβ1-42 and phosphorylated tau, hippocampal volume, and VGF peptide levels outperformed conventional biomarkers alone (hazard ratio=2.2 vs. 3.9), for predicting MCI to AD conversion.

CONCLUSIONS

CSF VGF enhances the ability of conventional biomarkers to predict MCI to AD conversion. Future work will be needed to determine the specificity of VGF for AD versus other neurodegenerative diseases.