ApoE Research Articles

Longitudinal trajectories of general cognitive and daily functions in data-driven subtypes of MCI: A longitudinal cohort analysis of older adults

ObjectivesTo derive data-driven subtypes of mild cognitive impairment (MCI) and characterize the complicated changes of general cognitive and daily functions over time in MCI subtypes. MethodsA total of 813 subjects diagnosed as MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included. Data-driven MCI subtypes were derived from group-based multi-trajectory modeling (GBMTM) analyses using longitudinal measurement scores in the cognitive domains of visuospatial function, language, and executive function. General cognitive and daily functions were measured by the Mini-Mental State Examination (MMSE) and the Functional Assessment Questionnaire (FAQ), respectively, whose longitudinal trajectory changes were depicted by Linear mixed models. ResultsThree MCI subtypes were derived, which were defined as “Cognitive decline group”, “Mild cognitive decline group” and “No cognitive decline group”. The “Mild cognitive decline group” had the highest percentage in the sample (46.2 %), followed by the “No cognitive decline group” (35.2 %). Patients in the “Cognitive decline group” had the highest mean age (74.69 years) at baseline, the highest APOE ε4 carriers (63.2 %), and the greatest dementia conversion rate (77.0 %). The changes in MMSE and FAQ score trajectories were fastest in the “Cognitive decline group” in the first 36 months and most slowly in the “No cognitive decline group”. ConclusionMCI individuals could be subdivided into more fine-grained cognitive subtypes, and identifying these distinct MCI subtypes and their different trajectories of cognitive decline may have important prognostic value for improving clinical course prediction.

Disease trajectories before dementia: evidence from a large-scale community-based prospective study.

Systemic changes in multiple diseases may influence the onset of dementia. However, the specific temporality between exposure diseases and dementia remains uncertain. By characterising the full spectrum of temporal disease trajectories before dementia, this study aims to yield a global picture of precursor diseases to dementia and to provide detailed instructions for risk management and primary prevention of dementia. Using the multicentre, community-based prospective UK Biobank, we constructed disease trajectories before dementia utilising the phenome-wide association analysis, paired directional test and association quantification. Stratified disease trajectories were constructed by dementia subtypes, gender, age of diagnosis and Apolipoprotein E (ApoE) status, respectively. Our study population comprised 434 266 participants without baseline dementia and 4638 individuals with all-cause dementia. In total, 1253 diseases were extracted as potential components of the disease trajectory before dementia. We identified three clusters of disease trajectories preceding all-cause dementia, initiated by circulatory, metabolic and respiratory diseases occurring approximately 5-15 years before dementia. Cerebral infarction or chronic renal failure following chronic ischaemic heart disease was the specific trajectory before vascular dementia. Apolipoprotein E (ApoE) ε4 non-carriers exhibited more complex trajectories compared with carriers. Lipid metabolism disorders remained in the trajectories regardless of dementia subtypes, gender, age of diagnosis and ApoE status. This study provides a comprehensive view of the longitudinal disease trajectories before dementia and highlights the potential targets of midlife cardiometabolic dysfunction for dementia screening and prevention.

Alzheimer's Disease: Exploring the Landscape of Cognitive Decline.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. The pathology of AD is marked by the accumulation of amyloid beta plaques and tau protein tangles in the brain, along with neuroinflammation and synaptic dysfunction. Genetic factors, such as mutations in APP, PSEN1, and PSEN2 genes, as well as the APOE ε4 allele, contribute to increased risk of acquiring AD. Currently available treatments provide symptomatic relief but do not halt disease progression. Research efforts are focused on developing disease-modifying therapies that target the underlying pathological mechanisms of AD. Advances in identification and validation of reliable biomarkers for AD hold great promise for enhancing early diagnosis, monitoring disease progression, and assessing treatment response in clinical practice in effort to alleviate the burden of this devastating disease. In this paper, we analyze data from the CAS Content Collection to summarize the research progress in Alzheimer's disease. We examine the publication landscape in effort to provide insights into current knowledge advances and developments. We also review the most discussed and emerging concepts and assess the strategies to combat the disease. We explore the genetic risk factors, pharmacological targets, and comorbid diseases. Finally, we inspect clinical applications of products against AD with their development pipelines and efforts for drug repurposing. The objective of this review is to provide a broad overview of the evolving landscape of current knowledge regarding AD, to outline challenges, and to evaluate growth opportunities to further efforts in combating the disease.

Multi-analyte proteomic analysis identifies blood-based neuroinflammation, cerebrovascular and synaptic biomarkers in preclinical Alzheimer’s disease

BackgroundBlood-based biomarkers are gaining grounds for the detection of Alzheimer’s disease (AD) and related disorders (ADRDs). However, two key obstacles remain: the lack of methods for multi-analyte assessments and the need for biomarkers for related pathophysiological processes like neuroinflammation, vascular, and synaptic dysfunction. A novel proteomic method for pre-selected analytes, based on proximity extension technology, was recently introduced. Referred to as the NULISAseq CNS disease panel, the assay simultaneously measures ~ 120 analytes related to neurodegenerative diseases, including those linked to both core (i.e., tau and amyloid-beta (Aβ)) and non-core AD processes. This study aimed to evaluate the technical and clinical performance of this novel targeted proteomic panel.MethodsThe NULISAseq CNS disease panel was applied to 176 plasma samples from 113 individuals in the MYHAT-NI cohort of predominantly cognitively normal participants from an economically underserved region in southwestern Pennsylvania, USA. Classical AD biomarkers, including p-tau181, p-tau217, p-tau231, GFAP, NEFL, Aβ40, and Aβ42, were independently measured using Single Molecule Array (Simoa) and correlations and diagnostic performances compared. Aβ pathology, tau pathology, and neurodegeneration (AT(N) statuses) were evaluated with [11C] PiB PET, [18F]AV-1451 PET, and an MRI-based AD-signature composite cortical thickness index, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA and neuroimaging-determined AT(N) biomarkers.ResultsNULISA concurrently measured 116 plasma biomarkers with good technical performance (97.2 ± 13.9% targets gave signals above assay limits of detection), and significant correlation with Simoa assays for the classical biomarkers. Cross-sectionally, p-tau217 was the top hit to identify Aβ pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878–0.983). Fourteen markers were significantly decreased in Aβ-PET + participants, including TIMP3, BDNF, MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aβ PET-dependent yearly increases in Aβ-PET + participants. Novel plasma biomarkers with tau PET-dependent longitudinal changes included proteins associated with neuroinflammation, synaptic function, and cerebrovascular integrity, such as CHIT1, CHI3L1, NPTX1, PGF, PDGFRB, and VEGFA; all previously linked to AD but only reliable when measured in cerebrospinal fluid. The autophagosome cargo protein SQSTM1 exhibited significant association with neurodegeneration after adjusting age, sex, and APOE ε4 genotype.ConclusionsTogether, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes, consistent with the recently revised biological and diagnostic framework. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.

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

BackgroundThe 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. MethodsA 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. ResultsWe 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). ConclusionOur 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.

Genetic Variants ε2 and ε4 of APOE Predict Mortality and Poor Outcome Independently in Spontaneous Intracerebral Hemorrhage Within the Chinese Han Population.

The heightened mortality and disability rates, coupled with restricted neurological recovery post intracerebral hemorrhage (ICH), have sparked considerable attention toward its treatment and results. Simultaneously, the influence of the APOE gene on ICH prognosis has been well-documented. This research aimed to explore the relationship between specific APOE alleles in the present cohort and the incidences of mortality, recurrence, and adverse prognosis, as determined by neurological function assessments in ICH patients. Data on patients diagnosed with ICH and hospitalized in the Department of Neurology at our institution from October 2021 to March 2022 were collected, including determining their APOE genotypes. A 1-year follow-up was conducted to evaluate mortality, ICH recurrence, and modified Rankin Scale (mRS) scores at 3 and 12 months. Poor prognosis was defined as an mRS score of ≥ 3. Initially, we analyzed the relationships between different APOE alleles and mortality, recurrence, and poor prognosis. Subsequently, we explored additional factors influencing each prognostic outcome and conducted multivariate analysis to identify independent risk factors. An analysis was conducted on 289 patients diagnosed with ICH. The presence of the ε2 allele was found to be a significant independent predictor for unfavorable outcomes at both 3 months (p = 0.022, OR = 2.138, 95% CI [2.041, 3.470]) and 1 year (p = 0.020, OR = 5.116, 95% CI [5.044, 5.307]). Moreover, the ε4 allele was established as an independent risk factor for ICH recurrence within 1 year (p = 0.025, OR = 2.326, 95% CI [1.163, 2.652]), as well as for mortality at 3 months (p = 0.037, OR = 4.250, 95% CI [4.068, 4.920]) and 1 year (p = 0.023, OR = 4.109, 95% CI [4.016, 4.739]). In conclusions, Both APOE ε2 and ε4 variants independently heighten mortality risk, recurrence, and poor prognosis after ICH. The substantial influence underscores the need for additional investigation into the impact of APOE genotype on ICH prognosis.

Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer’s disease continuum

Alzheimer’s disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the “diffuse-AD” (R1) dimension shows widespread brain atrophy, and the “MTL-AD” (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were “druggable genes” for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOEε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction—driven by genes different from APOE—which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/.

Impact of APOE ε4 and ε2 on plasma neurofilament light chain and cognition in autosomal dominant Alzheimer’s disease

BackgroundApolipoprotein E (APOE) genotypes have been suggested to influence cognitive impairment and clinical onset in presenilin-1 (PSEN1) E280A carriers for autosomal dominant Alzheimer’s disease (ADAD). Less is known about their impact on the trajectory of biomarker changes. Neurofilament light chain (NfL), a marker of neurodegeneration, begins to accumulate in plasma about 20 years prior to the clinical onset of ADAD. In this study we investigated the impact of APOE ε4 and ε2 variants on age-related plasma NfL increases and cognition in PSEN1 E280A mutation carriers.MethodsWe analyzed cross-sectional data from PSEN1 E280A mutation carriers and non-carriers recruited from the Alzheimer’s Prevention Initiative Registry of ADAD. All participants over 18 years with available APOE genotype, plasma NfL, and neuropsychological evaluation were included in this study. APOE genotypes and plasma NfL concentrations were characterized for each participant. Cubic spline models using a Hamiltonian Markov chain Monte Carlo method were used to characterize the respective impact of at least one APOE ε4 or ε2 allele on age-related log-transformed plasma NfL increases. Linear regression models were estimated to explore the impact of APOE ε4 and ε2 variants and plasma NfL on a composite cognitive test score in the ADAD mutation carrier and non-carrier groups.ResultsAnalyses included 788 PSEN1 E280A mutation carriers (169 APOE ε4 + , 114 ε2 +) and 650 mutation non-carriers (165 APOE ε4 + , 80 ε2 +), aged 18–75 years. APOE ε4 allele carriers were distinguished from ε4 non-carriers by greater age-related NfL elevations in the ADAD mutation carrier group, beginning about three years after the mutation carriers’ estimated median age at mild cognitive impairment onset. APOE ε2 allele carriers had lower plasma NfL concentrations than ε2 non-carriers in both the ADAD mutation carrier and non-carrier groups, unrelated to age, and an attenuated relationship between higher NfL levels on cognitive decline in the ADAD mutation carrier group.ConclusionsAPOE ε4 accelerates age-related plasma NfL increases and APOE ε2 attenuates the relationship between higher plasma NfL levels and cognitive decline in ADAD. NfL may be a useful biomarker to assess clinical efficacy of APOE-modifying drugs with the potential to help in the treatment and prevention of ADAD.

BEATRICE: Bayesian fine-mapping from summary data using deep variational inference.

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to sample from the space of causal configurations, which we use to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and noise paradigms, as defined by the relative genetic contributions of causal and noncausal variants. We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer's disease. In comparison to the baselines, only BEATRICE can successfully find the APOE ϵ2 allele, a commonly associated variant of Alzheimer's. BEATRICE is available for download at https://github.com/sayangsep/Beatrice-Finemapping.

APOE contributes to longitudinal impulse control disorders progression in Parkinson’s disease

BackgroundImpulse control disorders (ICDs) are an increasingly recognized complication in Parkinson disease (PD). The pathogenesis of ICDs is currently unclear. Few genetic studies have been conducted in this area.ObjectiveWe aimed to ascertain the correlation between APOE and ICDs, and identify clinical predictors of ICDs in PD.MethodsThis study included 287 PD patients from the Parkinson’s Progression Markers Initiative. They were followed up to investigate the progression of ICDs over a period of 5 years. The cumulative incidence of ICDs and potential risk factors were evaluated using Kaplan-Meier and Cox regression analyses.Results44.3% (31/70) patients with APOE ɛ4 and 32.3% (70/217) patients without APOE ɛ4 developed ICDs during the five-year follow up period. There were significant differences between the PD with and without ICDs development group in age, MSEADLG score, ESS score, GDS score, and STAI score at baseline. In multivariable Cox regression analysis, APOE ε4 (HR = 1.450, p = 0.048) and STAI score (HR = 1.017, p = 0.001) were predictors of the development of ICDs. Patients with APOE ɛ4 group showed significantly lower CSF Aβ42 and CSF α-syn level than patients without APOE ɛ4 group at baseline. In patients with APOE ɛ4 group, the “low α-syn level” group and the “low ptau/tau ratio” group had a significantly higher incidence of ICDs, respectively.ConclusionsThis study provides important insights into the potential role of the APOE gene in the development of ICDs in PD. Further studies are needed to confirm our findings and to investigate the underlying mechanisms in more detail.

Sex-Dependent Effects of Cardiometabolic Health and APOE4 on Brain Age: A Longitudinal Cohort Study.

The aging population is growing faster than all other demographic strata. With older age comes a greater risk of health conditions such as obesity and high blood pressure (BP). These cardiometabolic risk factors (CMRs) exhibit prominent sex differences in midlife and aging, yet their influence on brain health in females vs males is largely unexplored. In this study, we investigated sex differences in relationships between BP, body mass index (BMI), and brain age over time and tested for interactions with APOE ε4 genotype (APOE4), a known genetic risk factor of Alzheimer disease. The sample included participants from 2 United Kingdom-based longitudinal birth cohorts, the Lothian Birth Cohort (1936) and Insight 46 (1946). Participants with MRI data from at least 1 time point were included to evaluate sex differences in associations between CMRs and brain age. The open-access software package brainageR 2.1 was used to estimate brain age for each participant. Linear mixed-effects models were used to assess the relationships between brain age, BMI, BP, and APOE4 status (i.e., carrier vs noncarrier) in males and females over time. The combined sample comprised 1,120 participants (48% female) with a mean age (SD) of 73 (0.72) years in the Lothian Birth Cohort and 71 (0.68) years in Insight 46 at the time point 1 assessment. Approximately 30% of participants were APOE4 carriers. Higher systolic and diastolic BP was significantly associated with older brain age in females only (β = 0.43-0.56, p < 0.05). Among males, higher BMI was associated with older brain age across time points and APOE4 groups (β = 0.72-0.77, p < 0.05). In females, higher BMI was linked to older brain age among APOE4 noncarriers (β = 0.68-0.99, p < 0.05), whereas higher BMI was linked to younger brain age among carriers, particularly at the last time point (β = -1.75, p < 0.05). This study indicates sex-dependent and time-dependent relationships between CMRs, APOE4 status, and brain age. Our findings highlight the necessity of sex-stratified analyses to elucidate the role of CMRs in individual aging trajectories, providing a basis for developing personalized preventive interventions.

Identifying Proteomic Prognostic Markers for Alzheimer's Disease with Survival Machine Learning: the Framingham Heart Study.

Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is challenging by their high dimensionality and inherent correlations. Our study analyzed 1128 plasma proteins, measured by the SOMAscan platform, from 858 participants 55 years and older (mean age 63 years, 52.9% women) of the Framingham Heart Study (FHS) Offspring cohort. We conducted regression analysis and machine learning models, including LASSO-based Cox proportional hazard regression model (LASSO) and generalized boosted regression model (GBM), to identify protein prognostic markers. These markers were used to construct a weighted proteomic composite score, the AD prediction performance of which was assessed using time-dependent area under the curve (AUC). The association between the composite score and memory domain was examined in 339 (of the 858) participants with available memory scores, and in an independent group of 430 participants younger than 55 years (mean age 46, 56.7% women). Over a mean follow-up of 20 years, 132 (15.4%) participants developed AD. After adjusting for baseline age, sex, education, and APOE ε4+ status, regression models identified 309 proteins (P ≤ 0.2). After applying machine learning methods, nine of these proteins were selected to develop a composite score. This score improved AD prediction beyond the factors of age, sex, education, and APOE ε4+ status across 15 to 25 years of follow-up, achieving its peak AUC of 0.84 in the LASSO model at the 22-year follow-up. It also showed a consistent negative association with memory scores in 339 participants (beta = -0.061, P = 0.046), 430 independent participants (beta = -0.060, P = 0.018), and the pooled 769 samples (beta = -0.058, P = 0.003). These findings highlight the utility of proteomic markers in improving AD prediction and emphasize the complex pathology of AD. The composite score may aid early AD detection and efficacy monitoring, warranting further validation in diverse populations.

Synergistic Effect between the APOE ε4 Allele with Genetic Variants of GSK3B and MAPT: Differential Profile between Refractory Epilepsy and Alzheimer Disease.

Temporal Lobe Epilepsy (TLE) is a chronic neurological disorder characterized by recurrent focal seizures originating in the temporal lobe. Despite the variety of antiseizure drugs currently available to treat TLE, about 30% of cases continue to have seizures. The etiology of TLE is complex and multifactorial. Increasing evidence indicates that Alzheimer's disease (AD) and drug-resistant TLE present common pathological features that may induce hyperexcitability, especially aberrant hyperphosphorylation of tau protein. Genetic polymorphic variants located in genes of the microtubule-associated protein tau (MAPT) and glycogen synthase kinase-3β (GSK3B) have been associated with the risk of developing AD. The APOE ε4 allele is a major genetic risk factor for AD. Likewise, a gene-dose-dependent effect of ε4 seems to influence TLE. The present study aimed to investigate whether the APOE ɛ4 allele and genetic variants located in the MAPT and GSK3B genes are associated with the risk of developing AD and drug-resistant TLE in a cohort of the Mexican population. A significant association with the APOE ε4 allele was observed in patients with AD and TLE. Additional genetic interactions were identified between this allele and variants of the MAPT and GSK3B genes.

Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification

AbstractMissing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder‐based imputation of missing key features of heterogeneous data that comprised tau‐PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history. The authors focused on extreme (≥40%) missing at random of key features which depend on AD progression; identified as the history of a mother having AD, APoE ε4 alleles, and clinical dementia rating. Along with features selected using traditional feature selection methods, latent features extracted from the denoising autoencoder are incorporated for subsequent classification. Using random forest classification with 10‐fold cross‐validation, robust AD predictive performance of imputed datasets (accuracy: 79%–85%; precision: 71%–85%) across missingness levels, and high recall values with 40% missingness are found. Further, the feature‐selected dataset using feature selection methods, including autoencoder, demonstrated higher classification score than that of the original complete dataset. These results highlight the effectiveness and robustness of autoencoder in imputing crucial information for reliable AD prediction in AI‐based clinical decision support systems.

Cardiovascular risk of dementia is associated with brain–behaviour changes in cognitively healthy, middle-aged individuals

Alzheimer’s Disease (AD) neuropathology start decades before clinical manifestations, but whether risk factors are associated with early cognitive and brain changes in midlife remains poorly understood. We examined whether AD risk factors were associated with cognition and functional connectivity (FC) between the Locus Coeruleus (LC) and hippocampus – two key brain structures in AD neuropathology – cross-sectionally and longitudinally in cognitively healthy midlife individuals. Neuropsychological assessments and functional Magnetic Resonance Imaging were obtained at baseline (N=210), and two-years follow-up (N=188). Associations of cognition and FC with apolipoprotein ε4 (APOE ε4) genotype, family history of dementia, and the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) score were investigated. Cross-sectionally, higher CAIDE scores were associated with worse cognition. Menopausal status interacted with the CAIDE risk on cognition. Furthermore, the CAIDE score significantly moderated the relationship between cognition and LC–Hippocampus FC. Longitudinally, the LC–Hippocampus FC decreased significantly over 2 years. These results suggest that cardiovascular risk of dementia is associated with brain–behaviour changes in cognitively healthy, middle-aged individuals.

B - 55 Diffusion Tensor Imaging Analysis of Former American Football Players Exposed to Repetitive Head Impacts

Abstract Objective Exposure to repetitive head impacts (RHI) has been linked to Chronic Traumatic Encephalopathy (CTE) and neuropathological alterations such as white matter shear injuries. In this study, we use diffusion-tensor imaging (DTI) to investigate in vivo white matter alterations among former American football players. We also investigate how age and factors associated with RHI exposure influence white matter integrity. Methods We analyzed data from the DIAGNOSE CTE Research Project, focusing on former American football players (n = 166). The measures included whole-brain Fractional Anisotropy (FA), reflecting water diffusion directionality in white matter, and FreeWater-corrected FA (FAt) accounting for extracellular free water, using FSL Tract-Based Spatial Statistics (TBSS). We performed linear regressions on FA and FAt with age, age of first exposure to football, and estimates of cumulative head impact index (CHII) scores of frequency, linear acceleration, and rotational force controlling for age, body mass index, race, education, and APOE e4 allele presence as covariates. Results Both FA (p &amp;lt; 0.00001) and FAt (p &amp;lt; 0.00001) decreased as age increased. Moreover, FA (p &amp;lt; 0.01) and FAt (p &amp;lt; 0.01) were significantly lower with an earlier age of first exposure to football. Finally, FAt decreased as CHII linear acceleration (p &amp;lt; 0.04) and rotational forces (p &amp;lt; 0.02) increased. Conclusion Our study reveals age-related declines in both FA and FAt and highlights that the duration and intensity of exposure to RHI have an impact on white matter microstructure later in life. Overall, these results underscore the impact of prolonged exposure to RHI on white matter microstructure.

Lifestyle, biological, and genetic factors related to brain iron accumulation across adulthood

Iron is necessary for many neurobiological mechanisms, but its overaccumulation can be harmful. Factors triggering age-related brain iron accumulation remain largely unknown and longitudinal data are insufficient. We examined associations between brain iron load and accumulation and, blood markers of iron metabolism, cardiovascular health, lifestyle factors (smoking, alcohol use, physical activity, diet), and ApoE status using longitudinal data from the IronAge study (n = 208, age = 20–79, mean follow-up time = 2.75 years). Iron in cortex and basal ganglia was estimated with magnetic resonance imaging using quantitative susceptibility mapping (QSM). Our results showed that (1) higher peripheral iron levels (i.e., composite score of blood iron markers) were related to greater iron load in the basal ganglia; (2) healthier diet was related to higher iron levels in the cortex and basal ganglia, although for the latter the association was significant only in younger adults (age = 20–39); (3) worsening cardiovascular health was related to increased iron accumulation; (4) younger ApoE ε4 carriers accumulated more iron in basal ganglia than younger non-carriers. Our results demonstrate that modifiable factors, including lifestyle, cardiovascular, and physiological ones, are linked to age-related brain iron content and accumulation, contributing novel information on potential targets for interventions in preventing brain iron-overload.

Apolipoprotein E and Alzheimer's Disease in Italian Population: Systematic Review and Meta-Analysis.

Objective: This meta-analysis with a systematic review was undertaken to assess the association between APOE allelic genotypes and the risk of Alzheimer's disease (AD) in the Italian population. Methods: The Web of Science, PubMed, and Scopus databases were searched until 15 November 2023. The odds ratio (OR) with a 95% confidence interval (CI) was calculated using fixed and random effect models, depending on the I2 statistic value. The systematic review and meta-analysis were conducted in agreement with the PRISMA guideline and registered with PROSPERO (CRD42023492580). Results: Our meta-analysis based on 15 studies revealed a higher risk of AD among Italian individuals carrying the APOE ε4 allele (OR = 3.60, 95% CI [2.90-4.47], p < 0.0001). The association of AD genotype APOE ε2ε4 (OR = 1.36, 95% CI [0.76-2.41], p = 0.29) was not statistically significant, while APOE ε3ε4 (OR = 3.43, 95% CI [2.95-3.99], p < 0.0001) has a high risk of AD development; the risk is more notably in the APOE ε4ε4 genotype (OR = 7.08, 95% CI [4.22-11.86], p < 0.0001). The APOE ε2 allele has a protective effect (APOE ε2 (OR = 0.47, 95% CI [0.29-0.74], p = 0.0013)), and similar results were achieved by APOE ε3 (OR = 0.49, 95% CI [0.37-0.65], p < 0.0001). Subgroup analysis of three areas of Italy (southern, northern, and center) revealed that that APOE ε4 allele was a risk factor with a higher OR in northern Italy (OR 4.22; 95% CI [3.46-5.16], p < 0.0001) compared to southern and center Italy (OR 3.02; 95% CI [2.28-4.01], p < 0.0001 and OR 3.97; 95% CI [1.37-11.56], p < 0.0001, respectively). As well, APOE ε4ε4 genotype carriers had a significantly higher OR in northern Italy (OR 9.69; 95% CI [4.94-18.99], p < 0.0001) compared to in southern and center Italy (OR 4.38; 95% CI [1.54-12.47], p < 0.0001 and OR 3.59; 95% CI [0.87-14.86], p < 0.0001, respectively). Conclusions: This systematic review with a meta-analysis of the Italian population on APOE alleles, genotyping, and AD incidence, highlights that individuals harboring APOE ε4 have a higher risk of developing AD compared to those with other alleles. It also supports the protective effect of the APOE ε2 allele against the progress of AD. The qualitative analysis on the complex genetic interactions influencing Alzheimer risk emphasizes the need for further research on genetic and environmental factors for effective prevention strategies.

Differences in Grey Matter Concentrations and Functional Connectivity between Young Carriers and Non-Carriers of the APOE ε4 Genotype.

Background: The pathophysiology of Alzheimer's disease (AD) may begin developing years or even decades prior to the manifestation of its first symptoms. The APOE ε4 genotype is a prominent genetic risk for AD that has been found to be associated with brain changes across the lifespan since early adulthood. Thus, studying brain changes that may occur in young adults with an APOE ε4 status is highly relevant. Objective: Examine potential differences in grey matter (GM) and functional connectivity (FC) in brains of cognitively healthy young APOE ε4 carriers and non-carriers, denoted here as ε4(+) and ε4(-), respectively. Methods: Three Tesla magnetic resonance imaging (MRI) brain scans were acquired from cognitively healthy young participants aged approximately 20 years (n = 151). Voxel-based morphometry (VBM) analysis was employed to identify potential structural differences in GM between ε4(+) and ε4(-). In a subsequent seed-based connectivity (SBC) analysis, brain regions that structurally differed in the VBM analysis were considered as seeds and correlated with all the remaining voxels across the brains to then measure the differences in FC between groups. Results: The VBM analysis suggested that ε4(+) (n = 28) had greater GM densities relative to ε4(-) (n = 123) in the left hippocampus and the left posterior insula (puncorr < 0.001). However, the effect did not survive the correction for multiple comparisons, suggesting minimal structural differences in this age range. In contrast, the SBC analysis indicated that ε4(+) exhibited significantly decreased FC between the left hippocampus and areas of the left middle temporal gyrus (n = 27) compared to ε4(-) (n = 102). These results remained significant after multiple comparisons (pFDR < 0.05). Lastly, no statistically significant differences in FC between groups were observed for the left insular seed (pFDR > 0.05). Discussion: These results suggest early structural and functional brain changes associated with the APOE ε4 genotype on young adults. Yet, they must be cautiously interpreted and contrasted with both older adults with genetic risk for AD and patients diagnosed with AD.

Blockade of STING activation alleviates microglial dysfunction and a broad spectrum of Alzheimer’s disease pathologies

Abnormal glial activation promotes neurodegeneration in Alzheimer’s disease (AD), the most common cause of dementia. Stimulation of the cGAS-STING pathway induces microglial dysfunction and sterile inflammation, which exacerbates AD. We showed that inhibiting STING activation can control microglia and ameliorate a wide spectrum of AD symptoms. The cGAS-STING pathway is required for the detection of ectopic DNA and the subsequent immune response. Amyloid-β (Aβ) and tau induce mitochondrial stress, which causes DNA to be released into the cytoplasm of microglia. cGAS and STING are highly expressed in Aβ plaque-associated microglia, and neuronal STING is upregulated in the brains of AD model animals. The presence of the APOE ε4 allele, an AD risk factor, also upregulated both proteins. STING activation was necessary for microglial NLRP3 activation, proinflammatory responses, and type-I-interferon responses. Pharmacological STING inhibition reduced a wide range of AD pathogenic features in AppNL-G-F/hTau double-knock-in mice. An unanticipated transcriptome shift in microglia reduced gliosis and cerebral inflammation. Significant reductions in the Aβ load, tau phosphorylation, and microglial synapse engulfment prevented memory loss. To summarize, our study describes the pathogenic mechanism of STING activation as well as its potential as a therapeutic target in AD.