Alzheimer's Disease Prediction Research Articles

Genome-wide transcriptome analysis of Aβ deposition on PET in a Korean cohort.

Despite the recognized importance of including ethnic diversity in Alzheimer's disease (AD) research, substantial knowledge gaps remain, particularly in Asian populations. RNA sequencing was performed on blood samples from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) to perform differential gene expression (DGE), gene co-expression network, gene-set enrichment, and machine learning analyses for amyloid beta (Aβ) deposition on positron emission tomography. DGE analysis identified 265 dysregulated genes associated with Aβ deposition and replicated three AD-associated genes in an independent Korean cohort. Network analysis identified two modules related to pathways including a natural killer (NK) cell-mediated immunity. Machine learning analysis showed the classification of Aβ positivity improved with the inclusion of gene expression data. Our results in a Korean population suggest Aβ deposition-associated genes are enriched in NK cell-mediated immunity, providing a better understanding of AD molecular mechanisms and yielding potential diagnostic and therapeutic strategies. Dysregulated genes were associated with amyloid beta (Aβ) deposition on positron emission tomography in a Korean cohort. Dysregulated genes in Alzheimer's disease were replicated in an independent Korean cohort. Gene network moduleswere associated with Aβ deposition. Natural killer (NK) cell proportion in blood was associated with Aβdeposition. Dysregulated genes were related to a NK cell-mediated immunity.

Prediction of Alzheimer’s disease with MRI image segmentation and multi-layered dynamic feed-forward neural network

Alzheimer's disease (AD) is a kind of dementia characterized by memory loss in early stages. Damage to the nerve in the brain responsible for learning, reasoning, and remembering (a cognitive function) causes these symptoms. Treatment and patient care are most successful when they follow a timely and exact diagnosis. In this research, a Multi-Layered Dynamic- Feed Forward Neural Network (MLD-FFNN) method is used to analyse Magnetic resonance imaging (MRI) images to make accurate AD predictions. The approach has three essential components: image preprocessing, image segmentation, and classification. MRI datasets are used in the study to categorize subjects as non-demented, very mildly demented, mildly demented, or moderately demented. The initial step, data preprocessing, improves the quality of MRI images. Histogram equalization was designated as one of the methods discussed. Images' contrast may be improved with the use of technique called histogram equalization. Segmentation of images follows after preprocessing. Here, MRI images must be segmented into informative sections. The Gaussian filter (GF) method is used for image segmentation. When smoothing or filtering images, a method is used. Finally, a MLD-FFNN method is utilized for categorization. The segmented MRI scans are presumably the input for this neural network, which is trained to predict whether or not a patient has AD. This research shows effectiveness for early detection and treatment of AD because of its potential for precise prediction. Based on experimental results, this investigation achieved higher accuracy than other methods. The automated AD classification has the potential to serve as a useful aid for medical professionals in making diagnoses of AD and determining the best course of therapy.

Transparent Insights into Alzheimer’s Progression: A Time-Aware Approach with Explainable AI

Alzheimer’s disease, acknowledged for its intricate and degenerative characteristics, presents considerable challenges, particularly among the elderly population. The relentless nature of Alzheimer’s, marked by the gradual deterioration of cognitive function, underscores the urgency to develop effective strategies for early diagnosis and intervention. Artificial intelligence has played a significant role in terms of disease diagnosis and treatments. However, it has got limited acceptance in the medical community due to its lack of transparency. This study aims to advance the understanding and prediction of Alzheimer’s disease progression through the integration of time-aware modeling and Explainable AI techniques. It makes significant contributions by addressing two key objectives in the context of Alzheimer’s disease. First, by including temporal aspects, it accurately depicts the pace at which relevant predictors change over time, thereby capturing the dynamic nature of Alzheimer’s disease. Second, by giving interpretable insights into the algorithm’s decision-making process, the study hopes to empower researchers and physicians. This approach not only enhances transparency but also builds trust in the model’s outcomes. The ADNI dataset, comprising 2980 observations, was employed for developing a prediction model using various machine learning classifiers. Among these classifiers, the Random Forest model emerged as the top performer, exhibiting superior accuracy, a high Coefficient of Determination (R2), and an impressive F1 score. To enhance interpretability, subsequent analyses utilized LIME and SHAP techniques. By combining time-aware modeling with Explainable AI methods, we seek to unravel the dynamic relationships within the dataset, providing transparent insights into the temporal evolution of Alzheimer’s disease. Thus, this paper contributes to the creation of a clinically relevant and practical model for monitoring Alzheimer’s disease progression that holds the potential for a deeper understanding of the evolving nature of the disease and paving the way for personalized and timely interventions.

The Moderating Effect of Serum Vitamin D on the Relationship between Beta-amyloid Deposition and Neurodegeneration.

Previous studies have reported that vitamin D deficiency increased the risk of Alzheimer's disease (AD) dementia in older adults. However, little is known about how vitamin D is involved in the pathophysiology of AD. Thus, this study aimed to examine the association and interaction of serum vitamin D levels with in vivo AD pathologies including cerebral beta-amyloid (Aβ) deposition and neurodegeneration in nondemented older adults. 428 Nondemented older adults were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease, a prospective cohort that began in 2014. All participants underwent comprehensive clinical assessments, measurement of serum 25-hydroxyvitamin D (25[OH]D), and multimodal brain imaging including Pittsburgh compound B (PiB) positron emission tomography and magnetic resonance imaging. Global PiB deposition was measured for the Aβ biomarker. Intracranial volume-adjusted hippocampal volume (HVa) was used as a neurodegeneration biomarker. Overall, serum 25(OH)D level was not associated with either Aβ deposition or HVa after controlling for age, sex, apolipoprotein E ε4 positivity, and vascular risk factors. However, serum 25(OH)D level had a significant moderating effect on the association between Aβ and neurodegeneration, with lower serum 25(OH)D level significantly exacerbating cerebral Aβ-associated hippocampal volume loss (B = 34.612, p = 0.008). Our findings indicate that lower serum vitamin D levels may contribute to AD by exacerbating Aβ-associated neurodegeneration in nondemented older adults. Further studies to explore the potential therapeutic effect of vitamin D supplementation on the progression of AD pathology will be necessary.

Exploratory Dual PET imaging of [18F] fluorodeoxyglucose and [11C]acetoacetate in type 2 diabetic nonhuman primates

Despite recent advancements in imaging (amyloid-PET & tau-PET) and fluid (Aβ42/Aβ40 & Aβ42/ptau) biomarkers, the current standard for in vivo assessment of AD, diagnosis and prediction of Alzheimer’s disease (AD) remains challenging. We demonstrated in nonhuman primates (NHP) that increased plasma and cerebrospinal fluid (CSF) glucose correlated with decreased CSF Aβ42 and CSF Aβ40, a hallmark of plaque promoting pathogenesis. Together, our findings demonstrate that altered glucose homeostasis and insulin resistance are associated with Aβ and amyloid in rodent and NHP models. This warranted further exploration into the dynamics of altered brain metabolism in the NHP model of T2D, cross referenced with CSF and blood-based AD markers. Preliminary dual PET ([11C]acetoacetate ([11C]AcAc) and [18F]fluorodeoxyglucose ([18F]FDG) imaging studies were conducted in an aged cohort of NHPs classified as T2D (n = 5) and pre-diabetic (n = 1) along with corresponding plasma and CSF samples for metabolite analysis. [11C]AcAc and [18F]FDG PET brain standard uptake values (SUV) were highly positively associated (r = 0.88, p = 0.02) in the T2D and pre-diabetic NHPs. Age was not significantly associated with brain SUV (age range 16.5–23.5 years old). Metabolic measures were positively correlated with brain [18F]FDG and CSF Aβ42:40 was positively correlated to fasting glucose values. Although our findings suggest moderate correlations, this study further elucidates that peripheral insulin resistance and poor glycemia control alter AD-related pathology, illustrating how T2D is a risk factor for AD.

Moderation of thyroid hormones for the relationship between amyloid and tau pathology

BackgroundAltered thyroid hormone levels have been associated with increased risk of Alzheimer's disease (AD) dementia and related cognitive decline. However, the neuropathological substrates underlying the link between thyroid hormones and AD dementia are not yet fully understood. We first investigated the association between serum thyroid hormone levels and in vivo AD pathologies including both beta-amyloid (Aβ) and tau deposition measured by positron emission tomography (PET). Given the well-known relationship between Aβ and tau pathology in AD, we additionally examined the moderating effects of thyroid hormone levels on the association between Aβ and tau deposition.MethodsThis cross-sectional study was conducted as part of the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) cohort. This study included a total of 291 cognitively normal adults aged 55 to 90. All participants received comprehensive clinical assessments, measurements for serum total triiodothyronine (T3), free triiodothyronine (fT3), free thyroxine (fT4), and thyroid-stimulating hormone (TSH), and brain imaging evaluations including [11C]-Pittsburgh compound B (PiB)- PET and [18F] AV-1451 PET.ResultsNo associations were found between either thyroid hormones or TSH and Aβ and tau deposition on PET. However, fT4 (p = 0.002) and fT3 (p = 0.001) exhibited significant interactions with Aβ on tau deposition: The sensitivity analyses conducted after the removal of an outlier showed that the interaction effect between fT4 and Aβ deposition was not significant, whereas the interaction between fT3 and Aβ deposition remained significant. However, further subgroup analyses demonstrated a more pronounced positive relationship between Aβ and tau in both the higher fT4 and fT3 groups compared to the lower group, irrespective of outlier removal. Meanwhile, neither T3 nor TSH had any interaction with Aβ on tau deposition.ConclusionOur findings suggest that serum thyroid hormones may moderate the relationship between cerebral Aβ and tau pathology. Higher levels of serum thyroid hormones could potentially accelerate the Aβ-dependent tau deposition in the brain. Further replication studies in independent samples are needed to verify the current results.

Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer's disease.

Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.

Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI

Background:The prediction of Alzheimer’s disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI). Methods:We analyzed data from 1,617 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques. Results:Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models’ ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures. Conclusion:This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n=166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.

AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.

A Transformer Approach for Cognitive Impairment Classification and Prediction.

Early classification and prediction of Alzheimer disease (AD) and amnestic mild cognitive impairment (aMCI) with noninvasive approaches is a long-standing challenge. This challenge is further exacerbated by the sparsity of data needed for modeling. Deep learning methods offer a novel method to help address these challenging multiclass classification and prediction problems. We analyzed 3 target feature-sets from the National Alzheimer Coordinating Center (NACC) dataset: (1) neuropsychological (cognitive) data; (2) patient health history data; and (3) the combination of both sets. We used a masked Transformer-encoder without further feature selection to classify the samples on cognitive status (no cognitive impairment, aMCI, AD)-dynamically ignoring unavailable features. We then fine-tuned the model to predict the participants' future diagnosis in 1 to 3 years. We analyzed the sensitivity of the model to input features via Feature Permutation Importance. We demonstrated (1) the masked Transformer-encoder was able to perform prediction with sparse input data; (2) high multiclass current cognitive status classification accuracy (87% control, 79% aMCI, 89% AD); (3) acceptable results for 1- to 3-year multiclass future cognitive status prediction (83% control, 77% aMCI, 91% AD). The flexibility of our methods in handling inconsistent data provides a new venue for the analysis of cognitive status data.

Plasma Leptin and Alzheimer Protein Pathologies Among Older Adults

Many epidemiologic studies have suggested that low levels of plasma leptin, a major adipokine, are associated with increased risk of Alzheimer disease (AD) dementia and cognitive decline. Nevertheless, the mechanistic pathway linking plasma leptin and AD-related cognitive decline is not yet fully understood. To examine the association of plasma leptin levels with in vivo AD pathologies, including amyloid-beta (Aβ) and tau deposition, through both cross-sectional and longitudinal approaches among cognitively unimpaired older adults. This was a longitudinal cohort study from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer Disease. Data were collected from January 1, 2014, to December 31, 2020, and data were analyzed from July 11 to September 6, 2022. The study included a total of 208 cognitively unimpaired participants who underwent baseline positron emission tomography (PET) scans for brain Aβ deposition. For longitudinal analyses, 192 participants who completed both baseline and 2-year follow-up PET scans for brain Aβ deposition were included. Plasma leptin levels as assessed by enzyme-linked immunosorbent assay. Baseline levels and longitudinal changes of global Aβ and AD-signature region tau deposition measured by PET scans. Among the 208 participants, the mean (SD) age was 66.0 (11.3) years, 114 were women (54.8%), and 37 were apolipoprotein E ε4 carriers (17.8%). Lower plasma leptin levels had a significant cross-sectional association with greater brain Aβ deposition (β = -0.04; 95% CI, -0.09 to 0.00; P = .046), while there was no significant association between plasma leptin levels and tau deposition (β = -0.02; 95% CI, -0.05 to 0.02; P = .41). In contrast, longitudinal analyses revealed that there was a significant association between lower baseline leptin levels and greater increase of tau deposition over 2 years (β = -0.06; 95% CI, -0.11 to -0.01; P = .03), whereas plasma leptin levels did not have a significant association with longitudinal change of Aβ deposition (β = 0.006; 95% CI, 0.00-0.02; P = .27). The present findings suggest that plasma leptin may be protective for the development or progression of AD pathology, including both Aβ and tau deposition.

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection.

Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of the patients and their family members will be lessened with an early diagnosis of AD. The automatic diagnosis technique is widely required due to the shortage of medical experts and eases the burden of medical staff. The automatic artificial intelligence (AI)-based computerized method can help experts achieve better diagnosis accuracy and precision rates. This study proposes a new automated framework for AD stage prediction based on the ResNet-Self architecture and Fuzzy Entropy-controlled Path-Finding Algorithm (FEcPFA). A data augmentation technique has been utilized to resolve the dataset imbalance issue. In the next step, we proposed a new deep-learning model based on the self-attention module. A ResNet-50 architecture is modified and connected with a self-attention block for important information extraction. The hyperparameters were optimized using Bayesian optimization (BO) and then utilized to train the model, which was subsequently employed for feature extraction. The self-attention extracted features were optimized using the proposed FEcPFA. The best features were selected using FEcPFA and passed to the machine learning classifiers for the final classification. The experimental process utilized a publicly available MRI dataset and achieved an improved accuracy of 99.9%. The results were compared with state-of-the-art (SOTA) techniques, demonstrating the improvement of the proposed framework in terms of accuracy and time efficiency.

Effect of cerebrovascular risk factors on the prediction of Alzheimer’s disease neuropathologic change using plasma biomarkers (N4.003)

Effect of cerebrovascular risk factors on the prediction of Alzheimer’s disease neuropathologic change using plasma biomarkers (N4.003)

334 Identification of novel plasma protein biomarkers for diagnosis and prediction of Alzheimer’s disease in African Americans

OBJECTIVES/GOALS: To identify novel panel of plasma protein biomarkers to improve prediction and diagnosis of Alzheimer’s disease (AD) for African Americans (AA), who are at greater risk of developing AD compared to non-Hispanic White individuals but are underrepresented in AD research. METHODS/STUDY POPULATION: Pre-existing plasma samples from 460 AA individuals with clinical diagnoses of AD, cognitively unimpaired (CU), mild cognitive impairment (MCI), or dementia with Lewy bodies (DLB) will undergo untargeted proteomics using the SomaScan assay, where modified single stranded DNA aptamers bind to protein targets which are quantified by DNA microarray. Protein expression levels will be compared between diagnostic groups to identify differentially expressed proteins. Additional clinical, genetic, and lifestyle factors will be compared with protein expression when available. Proteins of interest, identified by differential protein expression analysis results, will be included in receiver operating characteristic analyses to identify the optimal set of proteins for diagnostic classification. RESULTS/ANTICIPATED RESULTS: A pilot experiment utilizing plasma from 40 individuals identified multiple differentially expressed proteins (DEPs) between AD and non-AD groups. Eight proteins were nominated from the differential protein analysis into a receiver operating characteristic (ROC) analysis based on pvalue and previous implication in AD genome wide association studies. Proteins involved in microglial activation, neuronal adhesion, cell proliferation, and innate immunity were nominated. The ROC model achieved 100% classification accuracy of AD and CU groups using age, sex, and the eight nominated proteins. It is expected that there will be more significant associations when utilizing the full cohort of 460 AA and that DEPs between AD, CU, MCI, and DLB will be identified. DISCUSSION/SIGNIFICANCE: The nomination of a novel panel of plasma biomarkers developed from an AA cohort will directly serve the AA community by improving access to an early and accurate diagnosis of AD. Access to improved prediction and diagnosis will likely improve disease management, thus improving patient outcomes and decreasing burden on families and caregivers.

Association between brain amyloid deposition and longitudinal changes of white matter hyperintensities

BackgroundGrowing evidence suggests that not only cerebrovascular disease but also Alzheimer’s disease (AD) pathological process itself cause cerebral white matter degeneration, resulting in white matter hyperintensities (WMHs). Some preclinical evidence also indicates that white matter degeneration may precede or affect the development of AD pathology. This study aimed to clarify the direction of influence between in vivo AD pathologies, particularly beta-amyloid (Aβ) and tau deposition, and WMHs through longitudinal approach.MethodsTotal 282 older adults including cognitively normal and cognitively impaired individuals were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aβ deposition, [18F] AV-1451 PET for measuring tau deposition, and MRI scans with fluid-attenuated inversion recovery image for measuring WMH volume. The relationships between Aβ or tau deposition and WMH volume were examined using multiple linear regression analysis. In this analysis, baseline Aβ or tau were used as independent variables, and change of WMH volume over 2 years was used as dependent variable to examine the effect of AD pathology on increase of WMH volume. Additionally, we set baseline WMH volume as independent variable and longitudinal change of Aβ or tau deposition for 2 years as dependent variables to investigate whether WMH volume could precede AD pathologies.ResultsBaseline Aβ deposition, but not tau deposition, had significant positive association with longitudinal change of WMH volume over 2 years. Baseline WMH volume was not related with any of longitudinal change of Aβ or tau deposition for 2 years. We also found a significant interaction effect between baseline Aβ deposition and sex on longitudinal change of WMH volume. Subsequent subgroup analyses showed that high baseline Aβ deposition was associated with increase of WMH volume over 2 years in female, but not in male.ConclusionsOur findings suggest that Aβ deposition accelerates cerebral WMHs, particularly in female, whereas white matter degeneration appears not influence on longitudinal Aβ increase. The results also did not support any direction of influence between tau deposition and WMHs.

Prediction of Alzheimer's Disease Based on 3D Genome Selected circRNA.

Alzheimer's disease (AD) is a neurodegenerative disease and there is by far no effective treatment for it, especially in its late stage. Circular RNAs (circRNAs), known as a class of non-coding RNAs are widely observed in eukaryotic transcriptomes, and are reported to play an important role in neurodegenerative diseases including AD. circRNAs usually act as microRNA (miRNA) inhibitors or «sponges» to regulate the function of miRNAs, leading to subsequent changes in protein activities and functions. Accumulating evidence indicates that circRNAs can serve as potential biomarker in AD early prediction. The functional roles of circRNAs are very versatile including miRNAs binding - thus affecting downstream gene expression, generating abnormally translated protein peptides, and affecting epigenetic modifications which subsequently affect AD related gene expressions. Therefore, identifying AD-related circRNAs can contribute to AD early diagnosis and intervention. In this work, we collected and curated an AD-related circRNA dataset; by exploring the circRNAs' corresponding DNA loci distribution in chromatin 3D conformation (3D genome) and utilize the such 3D genome information, we were able to selected a concise yet predictively effective circRNA panel, based on which, significantly better AD prediction machine learning models were achieved.

Association between Grip Strength and Future Cognitive Function in male

AbstractBackgroundFrailty is an aging‐related syndrome of physiological decline, and has been associated with cognitive function.Grip strength, could be used as a marker for frailty and older adult health status. Frailty has been associated with cognitive function. However, its mechanism has not been fully investigated.We aimed to investigate the association between grip strength with cognitive function.MethodWe prospectively recruited 184 participants who underwent a grip strength test, cognitive function test. Data was collected from the Korean Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE), an ongoing prospective cohort study which started in 2014. Frailty was represented by grip strength (kg). Cognitive function was assessed using Mini‐Mental State Examination(MMSE) and The Consortium to Establish a Registry for Alzheimer’s Disease‐K(CERAD‐K).Multiple linear regression models were used to analyze the associations between grip strength and cognitive function. We adjusted age, gender, years of education, and BMI.ResultAmong 184 participants, 119 (64.7%) were cognitively normal. 32 (17.4%) were diagnosed with mild cognitive impairment and 33 (17.9) were a probable AD. Mean age of the participants was 69.4 (8.2) years. 43.5% of the participants were female. Grip strength was positively associated with MMSE at 1 year later in male (ß = 0.182 p = 0.004). In terms of cognitive domain, verbal and visual memory, language, visuospatial, and frontal‐executive functions were related to grip strength (vebal memory: ß = 0.037 p = 0.022, visual memory: ß = 0.046 p = 0.002, language: ß = 0.055 p<0.001, visuospatial: ß = 0.068 p = 0.0213, frontal‐executive: ß = 0.050 p<0.001).ConclusionWeakness of grip strength is associated with decreased future cognitive function. This support concept that grip strength could be a marker for cognitive decline.

Association of Polygenic Risk Score of Alzheimer’s disease with A/T/N Biomarkers and Cognitive Decline

AbstractBackgroundPolygenic risk scores (PRS) quantify the combined effects of genetic variants to enable the prediction of an individual’s risk of developing the disease. Alzheimer’s disease (AD) is a complex disorder in which the relationship among PRS, neuropathological substrates, and related cognitive decline remain especially understudied in Asian populations. We investigated the associations of PRS with AD neuroimaging biomarkers and cognitive phenotypes in a Korean Cohort.MethodThe study included 501 older adults (264 cognitively normal [CN], 146 mild cognitive impairment [MCI], and 91 AD dementia) from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE). Comprehensive clinical and neuropsychological evaluations, as well as multi‐modal neuroimaging including 3T MRI and 11C‐PiB‐PET were administered. A subset of participants underwent 18F‐AV‐1451 PET (n = 167). Imaging phenotypes including beta‐amyloid (Aβ) positivity, global tau deposition, and adjusted hippocampal volume (HVa) were used for amyloid (A), tau (T), and neurodegeneration (N) biomarkers, respectively., which was imputed using the TOPMed reference panel. PRS was calculated using the summary statistics of a recent large‐scale GWAS study for AD by PRS‐CS. The Apolipoprotein E (APOE) region was excluded in the calculation. The associations between standardized PRS and imaging/cognitive phenotypes were examined using either multiple linear or logistic regression analysis with covariates (age and sex for neuroimaging biomarker phenotypes; age, sex and education for cognitive phenotypes).ResultPRS was significantly higher in the AD dementia group compared to the CN group. Higher PRS was associated with greater CDR‐sum‐of‐boxes (β[SE] = 0.233[0.083], t = 2.818, p = 0.005) and lower MMSE scores (β[SE] = ‐0.648[0.198], t = ‐3.271, p = 0.001). In terms of AD neuroimaging biomarkers (Figure 1), higher PRS was significantly associated with an increased likelihood of Aβ positivity (OR = 1.233, 95%CI = 1.024‐1.491), and lower HVa (β[SE] = ‐132.79[49.624], t = ‐2.676, p = 0.008), but not with global tau deposition (β[SE] = 0.075[0.048], t = 1.573, p = 0.118).ConclusionThese findings suggest that higher PRS predicts increased pathological Aβ deposition and accelerated neurodegeneration, as well as greater cognitive decline. Further investigation in larger and multiethnic samples are needed to determine generalizability to other ethnoracial populations.

Association between brain amyloid deposition and longitudinal changes of white matter hyperintensities

AbstractBackgroundWhite matter injury had been mostly considered a result of chronic ischemia caused by cerebral small vessel disease. However, recent studies reported that white matter degeneration could also be an important pathophysiological feature in Alzheimer’s disease (AD). This study aimed to identify the relationship between AD biomarkers, particularly amyloid‐beta (Aβ) and tau deposition, with white matter hyperintensities (WMHs) representing cerebral white matter injury.MethodA total 456 older adults aging from 55 to 90 years including cognitively normal and cognitively impaired individuals were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aβ deposition, [18F] AV‐1451 PET for measuring tau deposition, and MRI scans with fluid‐attenuated inversion recovery image for measuring WMH volume. For PET scans, standard uptake value ratio (SUVR) was used for the analyses; combined regions of hemispheric white matter, inferior cerebellum and pons were used as the reference region when obtaining SUVRs. The relationships between Aβ or tau deposition and WMH volume were examined using multiple regression analysis. In this analysis, baseline Aβ or tau were used as independent variables, and baseline as well as change of WMH volume over 2 years were used as dependent variables.ResultBaseline Aβ deposition levels had significant positive association with longitudinal change of WMH volume over 2 years (β = 0.238, p = 0.009), but not with WMH volume at baseline. Baseline tau was not related with any of WMH volume at baseline and longitudinal change of WMH volume over 2 years. We also found significant interaction effect between baseline Aβ deposition level and sex on longitudinal change of WMH volume. Subsequent subgroup analyses showed that high baseline Aβ deposition was associated with increase of WMH volume over 2 years in female (β = 0.427, p = 0.001), but not in male.ConclusionOur findings suggest that Aβ deposition, but not tau, accelerates cerebral white matter injury, particularly in female.