Preclinical Stage Research Articles

MiR-519a-3p, found to regulate cellular prion protein during Alzheimer's disease pathogenesis, as a biomarker of asymptomatic stages

Clinical relevance of miRNAs as biomarkers is growing due to their stability and detection in biofluids. In this, diagnosis at asymptomatic stages of Alzheimer's disease (AD) remains a challenge since it can only be made at autopsy according to Braak NFT staging. Achieving the objective of detecting AD at early stages would allow possible therapies to be addressed before the onset of cognitive impairment. Many studies have determined that the expression pattern of some miRNAs is dysregulated in AD patients, but to date, none has been correlated with downregulated expression of cellular prion protein (PrPC) during disease progression. That is why, by means of cross studies of miRNAs up-regulated in AD with in silico identification of potential miRNAs-binding to 3′UTR of human PRNP gene, we selected miR-519a-3p for our study. Then, in vitro experiments were carried out in two ways. First, we validated miR-519a-3p target on 3′UTR-PRNP, and second, we analyzed the levels of PrPC expression after using of mimic technology on cell culture. In addition, RT-qPCR was performed to analyzed miR-519a-3p expression in human cerebral samples of AD at different stages of disease evolution. Additionally, samples of other neurodegenerative diseases such as other non-AD tauopathies and several synucleinopathies were included in the study.Our results showed that miR-519a-3p overlaps with PRNP 3′UTR in vitro and promotes downregulation of PrPC. Moreover, miR-519a-3p was found to be up-regulated exclusively in AD samples from stage I to VI, suggesting its potential use as a novel label of preclinical stages of the disease.

Impaired lexical access for unique entities in individuals with subjective cognitive decline

Subjective cognitive decline (SCD) may serve as an early indicator of Alzheimer’s disease (AD). However, accurately quantifying cognitive impairment in SCD is challenging, mainly because existing assessment tools lack sensitivity. This study examined how tasks specifically designed to assess knowledge of famous people, could potentially aid in identifying cognitive impairment in SCD. A total of 60 adults with SCD and 60 healthy controls (HCs) aged 50 to 82 years performed a famous people verbal fluency task and a famous people naming task. In the famous people fluency task, the results showed that the individuals with SCD produced significantly fewer famous names in the total time allowed than the HCs, and this difference was also found in the first and the second time interval. In the famous people naming task, the performance of the SCD group was significantly lower than that of the HC group only in the more recent period of fame. Overall, these results suggest that retrieving the names of famous people was more difficult for people with SCD than for people without cognitive complaints. They also suggest that famous people verbal fluency and naming tasks could be useful in detecting cognitive decline at the preclinical stage of AD.

Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction.

Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described from four perspectives: absorption, distribution, metabolism and excretion-all of which are closely related to a fifth perspective, toxicity (ADMET). Since obtaining ADMET data from in vitro,in vivo or pre-clinical stages is time consuming and expensive, many efforts have been made to predict ADMET properties via computational approaches. However, the majority of available methods are limited in their ability to provide pharmacokinetics and toxicity for diverse targets, ensure good overall accuracy, and offer ease of use, interpretability and extensibility for further optimizations. Here, we introduce Deep-PK, a deep learning-based pharmacokineticand toxicity prediction, analysis and optimization platform. We applied graph neural networks and graph-based signatures as a graph-level feature to yield the best predictive performance across 73 endpoints, including 64 ADMET and 9 general properties. With these powerful models, Deep-PK supports molecular optimization and interpretation, aiding users in optimizing and understanding pharmacokinetics and toxicity for given input molecules. The Deep-PK is freely available athttps://biosig.lab.uq.edu.au/deeppk/.

Alzheimer’s disease classification algorithm based on fusion of channel attention and densely connected networks

Mild cognitive impairment (MCI) is a syndrome that occurs in the preclinical stage of Alzheimer’s disease (AD) and is also an early signal of the onset of AD. Early detection and accurate differentiation between MCI and AD populations, and providing them with effective intervention and treatment, are of great significance for preventing or delaying the onset of AD. In this paper, we propose a deep learning model, SE-DenseNet, that combines channel attention and dense connectivity networks and apply it to the field of magnetic resonance imaging (MRI) data recognition for the diagnosis of AD and MCI. First, to extract MRI features with high quality, a slicing algorithm based on two-dimensional image information entropy is proposed to obtain AD brain lesion features with stronger representation ability. Second, in terms of model structure, SENet is introduced as a channel attention module and redistribute the weight of image features in the channel dimension; use DenseNet as the main architecture to maximize information flow, and each layer is directly interconnected with subsequent layers. It enables the network to learn and extract relevant features from the input data and improve the classification ability of the network. Finally, our proposed model is validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the results have shown that the accuracy for the four classification tasks of AD-NC, AD-MCI, NC-MCI, and AD-NC-MCI can reach 98.12%, 97.42%, 97.42%, and 95.24%, respectively. At the same time, the sensitivity and specificity have also achieved satisfactory results, exhibited a high performance in comparison with the classic machine learning algorithm and several existing state-of-the-art deep learning methods, demonstrating the proposed method is a powerful tool for the early diagnosis and detection of AD.

Natural polymer hydrogel based 3D printed bioreactor testing platform for cancer cell culture

Although two-dimensional cell cultures provide a time- and cost-effective method for testing drugs at the preclinical level, they do not capture the three-dimensional cellular interactions or tumor penetration that must occur in vivo. Therefore, these drugs often fail as they transition from two-dimensional culture models to more complex ones, including animal models or even human clinical trials. The goal of this research was to develop a three-dimensional bioprinted pancreatic cancer drug testing platform that could increase the effectiveness of drug testing during the early preclinical stages. Specifically, different natural polymer hydrogel formulations of cellulose, alginate, and gelatin were tested to find the optimal printability and cell viability. It was determined that a cellulose nanocrystal and alginate hydrogel provided the best printability because of its superior shear thinning properties. In addition, BxPC-3 cells, that were printed and then cultured within this hydrogel for four days, exhibited a range of cell viability between 80 % and 60 %. To simulate vasculature around the bioprinted cultures, a spinning bioreactor was manufactured; however, it disturbed the cells, leading to a decrease in cell viability compared to stagnant cultures. Overall, the platform demonstrated good printability and cell viability for future use in pancreatic cancer drug testing.

An Era of Digital Healthcare-A Comprehensive Review of Sensor Technologies and Telehealth Advancements in Chronic Heart Failure Management.

Heart failure (HF) is a multi-faceted, complex clinical syndrome characterized by significant morbidity, high mortality rate, reduced quality of life, and rapidly increasing healthcare costs. A larger proportion of these costs comprise both ambulatory and emergency department visits, as well as hospital admissions. Despite the methods used by telehealth (TH) to improve self-care and quality of life, patient outcomes remain poor. HF management is associated with numerous challenges, such as conflicting evidence from clinical trials, heterogeneity of TH devices, variability in patient inclusion and exclusion criteria, and discrepancies between healthcare systems. A growing body of evidence suggests there is an unmet need for increased individualization of in-hospital management, continuous remote monitoring of patients pre and post-hospital admission, and continuation of treatment post-discharge in order to reduce re-hospitalizations and improve long-term outcomes. This review summarizes the current state-of-the-art for HF and associated novel technologies and advancements in the most frequently used types of TH (implantable sensors), categorizing devices in their preclinical and clinical stage, bench-to-bedside implementation challenges, and future perspectives on remote HF management to improve long-term outcomes of HF patients. The Review also highlights recent advancements in non-invasive remote monitoring technologies demonstrated by a few pilot observational prospective cohort studies.

Computer Аssessment of the Risk of Нerniated Protrusions of the lntervertebral Discs of the Neck Based on MRl Data

The purpose of the research is assessment of the risk of herniated protrusions of intervertebral discs C4-C5, C5-C6 and C6-C7 of the cervical spine according to MR/ examination.Methods. Тhe biomechanics of the physiological processes of this department has been studied on the basis of MR/ data. A computer program «A method for assessing the risk of herniated protrusions of intervertebral discs of the cervical spine based on the results of an MRI examination» has been developed and tested. The measurements required for the above assessment were obtained during the evaluation of the MR/ examination of a particular patient, after which the data was entered into the program. Тhe calculation results were compared with the database previously obtained by the authors in the course of experimental and clinical studies.Results. Тhe model we use correctly describes the mechanical features of the vertebral junction, since it takes into account the most significant features of their structure, which affect the change in the length of the arms of the levers and the force of pressure on the center of the intervertebral disc. Based on magnetic resonance imaging data, the height of the intervertebral disc at the level of the anterior edges of the vertebral bodies and the АV value were determined, and tg was calculated. For a particular patient, the risk of intervertebral disc protrusion was obtained at the C4-C5, C5-C6 and C6-C7 levels.Conclusion. Тhe test results of the program for assessing the risk of developing an intervertebral disc protrusion in the cervical spine make it possible to describe dynamic phenomena in the cervical spine and predict an unfavorable outcome of pathology at the preclinical stage, even in the absence of clinical manifestations.

Knowledge Transfer and Networking Upon Implementation of a Transdisciplinary Digital Health Curriculum in a Unique Digital Health Training Culture: Prospective Analysis.

Digital health has been taught at medical faculties for a few years. However, in general, the teaching of digital competencies in medical education and training is still underrepresented. This study aims to analyze the objective acquisition of digital competencies through the implementation of a transdisciplinary digital health curriculum as a compulsory elective subject at a German university. The main subject areas of digital leadership and management, digital learning and didactics, digital communication, robotics, and generative artificial intelligence were developed and taught in a transdisciplinary manner over a period of 1 semester. The participants evaluated the relevant content of the curriculum regarding the competencies already taught in advance during the study, using a Likert scale. The participants' increase in digital competencies were examined with a pre-post test consisting of 12 questions. Statistical analysis was performed using an unpaired 2-tailed Student t test. A P value of <.05 was considered statistically significant. Furthermore, an analysis of the acceptance of the transdisciplinary approach as well as the application of an alternative examination method (term paper instead of a test with closed and open questions) was carried out. In the first year after the introduction of the compulsory elective subject, students of human medicine (n=15), dentistry (n=3), and medical biotechnology (n=2) participated in the curriculum. In total, 13 participants were women (7 men), and 61.1% (n=11) of the participants in human medicine and dentistry were in the preclinical study stage (clinical: n=7, 38.9%). All the aforementioned learning objectives were largely absent in all study sections (preclinical: mean 4.2; clinical: mean 4.4; P=.02). The pre-post test comparison revealed a significant increase of 106% in knowledge (P<.001) among the participants. The transdisciplinary teaching of a digital health curriculum, including digital teaching methods, considers perspectives and skills from different disciplines. Our new curriculum facilitates an objective increase in knowledge regarding the complex challenges of the digital transformation of our health care system. Of the 16 student term papers arising from the course, robotics and artificial intelligence attracted the most interest, accounting for 9 of the submissions.

Анализ кератотопографических и биомеханических показателей роговицы c оценкой влияния результатов диагностики на тактику ведения пациента с миопической рефракцией

Abstract Analysis of keratopographic and biomechanical corneal indicators with evaluation of the impact of diagnostic results on myopic refraction patient management tactics D.D. Sibakin, V.A. Breev, E.G. Solodkova S. Fyodorov Eye Microsurgery Federal State Institution, Volgograd Branch, Volgograd, Russian Federation Purpose. To analyse keratopographic and biomechanical corneal indicators with the diagnostic results assessment of the influence on the treatment tactics of myopic refraction patient. Material and methods. Keratotopographic and biomechanical parameters of the cornea in a patient with mild myopia were evaluated. Results. The Belin/Ambrosio (Enchanced Ectasia Display) – BAD keratoconus screening program was used on Pentacam AXL, which did not reveal corneal pathology. According to the results of CBI (Corvis biomechanical index) on Corvis ST (Oculus, Germany) (OD – 0.61, OS – 0.48), that was indicating poor corneal biomechanical properties and a high risk of keratectasia development. Conclusion. Ophthalmic examination, using The Corvis ST biomechanical measurements, makes possible to reveal with greater sensitivity subclinical signs of a keratectatic process at the pre-clinical stage of development. Key words: keratotopography, corneal biomechanics, keratoconus

A molecular journey on the pathogenesis of primary hyperoxaluria.

Primary hyperoxalurias (PHs) are rare disorders caused by the deficit of liver enzymes involved in glyoxylate metabolism. Their main hallmark is the increased excretion of oxalate leading to the deposition of calcium oxalate stones in the urinary tract. This review describes the molecular aspects of PHs and their relevance for the clinical management of patients. Recently, the study of PHs pathogenesis has received great attention. The development of novel in vitro and in vivo models has allowed to elucidate how inherited mutations lead to enzyme deficit, as well as to confirm the pathogenicity of newly-identified mutations. In addition, a better knowledge of the metabolic consequences in disorders of liver glyoxylate detoxification has been crucial to identify the key players in liver oxalate production, thus leading to the identification and validation of new drug targets. The research on PHs at basic, translational and clinical level has improved our knowledge on the critical factors that modulate disease severity and the response to the available treatments, leading to the development of new drugs, either in preclinical stage or, very recently, approved for patient treatment.

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review

BackgroundIslet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies.MethodsWe systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment.ResultsHere we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation.ConclusionsCurrent evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.

Evaluating p-tau217 and p-tau231 as Biomarkers for Early Diagnosis and Differentiation of Alzheimer's Disease: A Narrative Review.

The escalating prevalence of Alzheimer's disease (AD) highlights the urgent need to develop reliable biomarkers for early diagnosis and intervention. AD is characterized by the pathological accumulation of amyloid-beta plaques and tau neurofibrillary tangles. Phosphorylated tau (p-tau) proteins, particularly p-tau217 and p-tau231, have been identified as promising biomarker candidates to differentiate the disease progression from preclinical stages. This narrative review is devoted to a critical evaluation of the diagnostic accuracy, sensitivity, and specificity of p-tau217 and p-tau231 levels in the detection of AD, measured in plasma, serum, and cerebrospinal fluid, compared to established biomarkers. Additionally, the efficacy of these markers in distinguishing AD from other neurodegenerative disorders is examined. The significant advances offered by p-tau217 and p-tau231 in AD diagnostics are highlighted, demonstrating their unique utility in early detection and differential diagnosis. This comprehensive analysis not only confirms the excellent diagnostic capabilities of these markers, but also deepens the understanding of the molecular dynamics of AD, contributing to the broader scientific discourse on neurodegenerative diseases. This review is aimed to provide key information for researchers and clinicians across disciplines, filling interdisciplinary gaps and highlighting the role of p-tau proteins in revolutionizing AD research and clinical practice.

Plasma Phosphorylated Tau 231 Increases at One-Year Intervals in Cognitively Unimpaired Subjects.

Plasma biomarkers of Alzheimer's disease (AD) constitute a non-invasive tool for diagnosing and classifying subjects. They change even in preclinical stages, but it is necessary to understand their properties so they can be helpful in a clinical context. With this work we want to study the evolution of p-tau231 plasma levels in the preclinical stages of AD and its relationship with both cognitive and imaging parameters. We evaluated plasma phosphorylated (p)-tau231 levels in 146 cognitively unimpaired subjects in sequential visits. We performed a Linear Mixed-effects Model to analyze their rate of change. We also correlated their baseline levels with cognitive tests and structural and functional image values. ATN status was defined based on cerebrospinal fluid biomarkers. Plasma p-tau231 showed a significant rate of change over time. It correlated negatively with memory tests only in amyloid-positive subjects. No significant correlations were found with any imaging measures. Increases in plasma p-tau231 can be detected at one-year intervals in cognitively healthy subjects. It could constitute a sensitive marker for detecting early signs of neuronal network impairment by amyloid.

Cerebral-cognitive reserve: concept and functions of the cerebral-cognitive reserve

IntroductionThe modern understanding of AD allows us to consider it through the constructs of “vulnerability” and “stability” of the brain as a dynamic system of dialectical interaction between the pathogenic process and the protective process that prevents neurodegeneration. The concept of cognitive reserve (CR) is based on observations of discrepancy between the degree of brain pathology and the severity of clinical manifestations. The concept of “reserve” was proposed to describe the resistance of the brain to a developing damage caused by a pathological process. Stern (2002) considered CR as a protective factor that modifies the impact of brain pathology on cognitive function. The researchers have defined CR as an ability to optimize cognitive function through differential involvement of structures or neural networks of the brain into brain activity.ObjectivesA systematic review of scientific studies has been conductedMethodsThe review includes an analysis of full-text literature sources.ResultsSeveral possible directions of CR influence on cognitive functions have been described:1)CR may reduce the risk of MCI or dementia through mechanisms, which do not depend on the level of neurodegenerative pathological changes in the brain.2)CR can interact with the markers of brain pathology or healthaffectingthe future cognitive decline or risk of progression. It has been found that smaller volumes or thickness in some AD vulnerable areas of the brain represent a stronger risk factor for cognitive impairment in people with low CR than in people with higher CR. CR protective effects on clinical outcomes reduce as the number of damaged neurons increases.3)The protective effect of CR increasesduring late AD onset and at a low rate of the damaged substrate accumulation.4)CR changes the relationship between genetic factors and aging withclinical and cognitive outcomes. The relationship between age and AD pathology level or age-related structural changes in the brain may weaken in people with higher CR ConclusionsThe concept of cerebral-cognitive reserve actualizes the problem of the search for compensatory mechanisms of cognitive deficit in AD, the assessmentof the structure of the reserve, the development and implementationof programs to maintain the reserve, the preventionof its depletion, starting from the preclinical stage of the disease, which can prevent the transformation of preclinical manifestations of AD into cognitive disordersDisclosure of InterestNone Declared

Brain Functional Connectivity Changes in Rapid Eye Movement Sleep Behavior Disorder

The idiopathic rapid eye movement sleep behavior disorder (iRBD) has been reported as prodromal biomarker for neurodegenerative synucleinopathies. To early detect neurodegeneration in preclinical stage, pathophysiological changes of iRBD have been studied in many ways including structural magnetic resonance imaging (MRI), functional MRI (fMRI), and connectivity analyses. This review summarizes functional connectivity studies using resting-state fMRI in iRBD patients focused on motor-related network, compensatory mechanism of synucleinopathies, cognitive dysfunction, and non-motor symptoms.

Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging

Neuroimaging studies often quantify tau burden in standardized brain regions to assess Alzheimer disease (AD) progression. However, this method ignores another key biological process in which tau spreads to additional brain regions. We have developed a metric for calculating the extent tau pathology has spread throughout the brain and evaluate the relationship between this metric and tau burden across early stages of AD. 445 cross-sectional participants (aged≥50) who had MRI, amyloid PET, tau PET, and clinical testing were separated into disease-stage groups based on amyloid positivity and cognitive status (older cognitively normal control, preclinical AD, and symptomatic AD). Tau burden and tau spatial spread were calculated for all participants. We found both tau metrics significantly elevated across increasing disease stages (p<0.0001) and as a function of increasing amyloid burden for participants with preclinical (p<0.0001, p=0.0056) and symptomatic (p=0.010, p=0.0021) AD. An interaction was found between tau burden and tau spatial spread when predicting amyloid burden (p=0.00013). Analyses of slope between tau metrics demonstrated more spread than burden in preclinical AD (β=0.59), but then tau burden elevated relative to spread (β=0.42) once participants had symptomatic AD, when the tau metrics became highly correlated (R=0.83). Tau burden and tau spatial spread are both strong biomarkers for early AD but provide unique information, particularly at the preclinical stage. Tau spatial spread may demonstrate earlier changes than tau burden which could have broad impact in clinical trial design. This research was supported by the Knight Alzheimer Disease Research Center (Knight ADRC, NIH grants P30AG066444, P01AG026276, P01AG003991), Dominantly Inherited Alzheimer Network (DIAN, NIH grants U01AG042791, U19AG03243808, R01AG052550-01A1, R01AG05255003), and the Barnes-Jewish Hospital Foundation Willman Scholar Fund.

Shifting our attention earlier in the multiple sclerosis disease course.

Revisions of multiple sclerosis (MS) diagnostic criteria enable clinicians to diagnose patients earlier in the biologic disease course. Prompt initiation of therapy correlates with improved clinical outcomes. This has led to increased attention on the earliest stages of MS, including the MS prodrome and radiologically isolated syndrome (RIS). Here, we review current understanding and approach to patients with preclinical MS. MS disease biology often begins well before the onset of typical MS symptoms, and we are increasingly able to recognize preclinical and prodromal stages of MS. RIS represents the best characterized aspect of preclinical MS, and its diagnostic criteria were recently revised to better capture patients at highest risk of conversion to clinical MS. The first two randomized control trials evaluating disease modifying therapy use in RIS also found that treatment could delay or prevent onset of clinical disease. Despite progress in our understanding of the earliest stages of the MS disease course, additional research is needed to systematically identify patients with preclinical MS as well as capture those at risk for developing clinical disease. Recent data suggests that preventive immunomodulatory therapies may be beneficial for high-risk patients with RIS; though management remains controversial.

Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study.

Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring. A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated. The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups. This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers.

A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease.

It has become consensus that mild cognitive impairment (MCI), one of the early symptoms onset of Alzheimer's disease (AD), may appear 10 or more years after the emergence of neuropathological abnormalities. Therefore, understanding the progression of AD biomarkers and uncovering when brain alterations begin in the preclinical stage, while patients are still cognitively normal, are crucial for effective early detection and therapeutic development. In this paper, we develop a Bayesian semiparametric framework that jointly models the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Furthermore, unlike most existing methods assuming that everyone in the considered population will eventually develop the disease, our approach accounts for the possibility that some individuals may never experience MCI or AD, even after a long follow-up time. We evaluate the proposed model through simulation studies and demonstrate its clinical utility by examining an important AD biomarker, ptau181, using a dataset from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study.

Association Between Years of Education and Amyloid Burden in Patients With Subjective Cognitive Decline, MCI, and Alzheimer Disease.

Higher-educated patients with Alzheimer disease (AD) can harbor greater neuropathologic burden than those with less education despite similar symptom severity. In this study, we assessed whether this observation is also present in potential preclinical AD stages, namely in individuals with subjective cognitive decline and clinical features increasing AD likelihood (SCD+). Amyloid-PET information ([18F]Flutemetamol or [18F]Florbetaben) of individuals with SCD+, mild cognitive impairment (MCI), and AD were retrieved from the AMYPAD-DPMS cohort, a multicenter randomized controlled study. Group classification was based on the recommendations by the SCD-I and NIA-AA working groups. Amyloid PET images were acquired within 8 months after initial screening and processed with AMYPYPE. Amyloid load was based on global Centiloid (CL) values. Educational level was indexed by formal schooling and subsequent higher education in years. Using linear regression analysis, the main effect of education on CL values was tested across the entire cohort, followed by the assessment of an education-by-diagnostic-group interaction (covariates: age, sex, and recruiting memory clinic). To account for influences of non-AD pathology and comorbidities concerning the tested amyloid-education association, we compared white matter hyperintensity (WMH) severity, cardiovascular events, depression, and anxiety history between lower-educated and higher-educated groups within each diagnostic category using the Fisher exact test or χ2 test. Education groups were defined using a median split on education (Md = 13 years) in a subsample of the initial cohort, for whom this information was available. Across the cohort of 212 individuals with SCD+ (M(Age) = 69.17 years, F 42.45%), 258 individuals with MCI (M(Age) = 72.93, F 43.80%), and 195 individuals with dementia (M(Age) = 74.07, F 48.72%), no main effect of education (ß = 0.52, 95% CI -0.30 to 1.58), but a significant education-by-group interaction on CL values, was found (p = 0.024) using linear regression modeling. This interaction was driven by a negative association of education and CL values in the SCD+ group (ß = -0.11, 95% CI -4.85 to -0.21) and a positive association in the MCI group (ß = 0.15, 95% CI 0.79-5.22). No education-dependent differences in terms of WMH severity and comorbidities were found in the subsample (100 cases with SCD+, 97 cases with MCI, 72 cases with dementia). Education may represent a factor oppositely modulating subjective awareness in preclinical stages and objective severity of ongoing neuropathologic processes in clinical stages.