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Background: Neuropsychiatric symptoms are uncommon at Alzheimer's Disease (AD) dementia diagnosis but are exhibited by nearly everyone during the course of dementia. Depressive symptoms are common in AD dementia. We sought to determine correlations between memory, executive functioning, language, neuropsychiatric symptoms, and depressive symptoms at AD dementia diagnosis, and to characterize neuropsychiatric and depressive symptoms across groups defined by substantial relative cognitive impairments, using data from the National Alzheimer's Coordinating Center (NACC). Method: Using confirmatory factor analysis, we derived composite Neuropsychiatric Inventory Questionnaire (NPI‐Q) and Geriatric Depression Scale (excluding the memory question) scores. We defined a reference category of people with AD dementia whose memory, executive functioning, and language scores were similar to each other. Other groups were defined based on relative differences in cognitive domain scores. We used multinomial logistic regression to test if neuropsychiatric and depressive symptoms differed across groups. Result: Our sample included the diagnosis visit for 7,747 people with AD dementia and CDR = 1 (Table 1). Correlations between cognitive domains ranged from +0.29 to +0.50. Correlations between cognitive domains and NPI‐Q composite scores ranged from ‐0.02 to +0.04 and with depressive symptoms from ‐0.09 to +0.14. The correlation between depressive symptoms and the NPI‐Q composite was +0.17. We did not find a difference in NPI‐Q composite scores across groups, but depressive symptom score differed across groups (p<0.0001), with higher scores in people with relatively greater executive functioning impairments and lower scores in people with relatively greater memory impairments, compared to people with similar domain scores (Figure 1, Table 2). Conclusion: Composite scores for depressive and neuropsychiatric symptoms facilitate well‐powered investigations of relationships with these clinically salient factors. Neuropsychiatric symptoms had trivial correlations with cognitive domain scores at AD dementia diagnosis, and did not differ across groups defined by substantial relative cognitive impairments. Depressive symptoms were weakly correlated with cognitive domain scores, and differed across groups defined by substantial relative cognitive impairments. More research is warranted to further characterize depression and neuropsychiatric symptoms over the course of AD dementia. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. METHODS: Given the genetic susceptibility of AD, a multi‐factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk‐stratified groups, focusing on patients with mild cognitive impairment (MCI). RESULTS: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4– MCI, and amyloid+ MCI. DISCUSSION: Our risk score holds great potential to improve the precision of early risk assessment. Highlights: Accurate early risk assessment is critical for the success of clinical trials.A new risk score was built from integrating amyloid imaging and genetic data.Our risk score demonstrated improved capability in early risk stratification. [ABSTRACT FROM AUTHOR]

INTRODUCTION: MicroRNAs (miRNAs) play important roles in gene expression regulation and Alzheimer's disease (AD) pathogenesis. METHODS: We investigated the association between baseline plasma miRNAs and central AD biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 803): amyloid, tau, and neurodegeneration (A/T/N). Differentially expressed miRNAs and their targets were identified, followed by pathway enrichment analysis. Machine learning approaches were applied to investigate the role of miRNAs as blood biomarkers. RESULTS: We identified nine, two, and eight miRNAs significantly associated with A/T/N positivity, respectively. We identified 271 genes targeted by amyloid‐related miRNAs with estrogen signaling receptor–mediated signaling among the enriched pathways. Additionally, 220 genes targeted by neurodegeneration‐related miRNAs showed enrichment in pathways including the insulin growth factor 1 pathway. The classification performance of demographic information for A/T/N positivity was increased up to 9% with the inclusion of miRNAs. DISCUSSION: Plasma miRNAs were associated with central A/T/N biomarkers, highlighting their potential as blood biomarkers. Highlights: We performed association analysis of microRNAs (miRNAs) with amyloid/tau/neurodegeneration (A/T/N) biomarker positivity.We identified dysregulated miRNAs for A/T/N biomarker positivity.We identified Alzheimer's disease biomarker‐specific/common pathways related to miRNAs.miRNAs improved the classification for A/T/N positivity by up to 9%.Our study highlights the potential of miRNAs as blood biomarkers. [ABSTRACT FROM AUTHOR]

A synaptic protein, Neuronal Pentraxin 2 (NPTX2), has emerged as a pivotal biomarker for Alzheimer's dementia (AD). We identified candidate miRNAs targeting NPTX2 and performed association and mediation analyses using multi‐omics data (N = 702). Among 44 candidate miRNAs, miR‐133b was significantly associated with AD and Braak positivity. Higher miR‐133b expression was also associated with higher NPTX2 gene expression and better cognition. Mediation analysis showed that miR‐133b partially influences AD and cognition through the NPTX2 protein. Our integrated approach suggests a potential role of miR‐133b in synaptic integrity and offers new insights into AD pathogenesis. [ABSTRACT FROM AUTHOR]

Background: There is growing evidence that epigenetic age acceleration may predict late life cognitive decline and dementia, but it is unknown whether this is due to accelerated neurodegeneration or reduction in cognitive resilience. We examined the relationship between epigenetic clocks and domain specific neuropsychological (NP) factor scores, mild cognitive impairment (MCI), Alzheimer's Disease (AD), and all‐cause dementia, before and after accounting for plasma total tau (t‐tau), a marker of neurodegeneration. Method: DNA methylation and plasma t‐tau (Simoa assay; Quanterix) data from 2091 Framingham Heart Study Offspring cohort participants were generated from blood at the same Exam 8 visit (2005‐2008). Three epigenetic clock measures: DunedinPACE, PC PhenoAge, and PC GrimAge were estimated from the DNA methylation data. Longitudinal NP factor scores were previously derived for memory, language, and executive function using confirmatory factor analysis. We tested the association of epigenetic age acceleration with cognitive trajectories using linear mixed effects models and with time to MCI, all‐cause dementia and AD using Cox‐proportional hazard models. Models were run with and without adjustment for plasma t‐tau. All models included APOE ε4‐carrier status, education, smoking, age, and sex as covariates. Epigenetic measures were standardized in all models. Result: At Exam 8, the sample was, on average, 66.3 (SD = 9.0) years of age, 54.8% female, and had 16.4 (SD = 2.7) years of education. DundeinPACE was significantly associated with faster decline in executive function (βtimeXepi_age = ‐0.005, 95% CI:[‐0.009,‐0.002], p = 0.0020), but not with baseline executive function. Older PhenoAge (βepi_age = ‐0.041, 95% CI:[‐0.067,‐0.014], p = 0.0028) and GrimAge (βepi_age = ‐0.042, 95% CI:[‐0.073,‐0.011], p = 0.0084) were significantly associated with worse baseline executive function, but not with rate of decline. Older PhenoAge also was significantly associated with worse baseline memory (βepi_age = ‐0.037, 95% CI:[‐0.061,‐0.012], p = 0.0036). DunedinPACE was significantly associated with time to MCI (HR = 1.20, 95% CI:[1.06,1.35], p = 0.0034), AD (HR = 1.30, 95% CI:[1.07,1.57], p = 0.0068) and all‐cause dementia (HR = 1.30, 95% CI:[1.10,1.53], p = 0.0017). Results remained similar after adjustment for plasma t‐tau. Conclusion: Epigenetic age acceleration may be a marker of cognitive resilience, particularly in executive function. Of the three epigenetic clocks examined, DundedinPACE showed the most robust associations with cognitive resilience, with lower DunedinPACE associated with greater cognitive resilience. [ABSTRACT FROM AUTHOR]

Background: Genome‐wide association studies (GWAS) in Alzheimer's disease (AD) leveraging endophenotypes beyond case/control diagnosis, such as brain amyloid β pathology, have shown promise in identifying novel variants and understanding their potential functional impact. In this study, we leverage two brain amyloid β pathology measurement modalities, PET imaging and neuropathology, to address sample size limitations and to discover novel genetic drivers of disease. Method: We conducted a meta‐analysis on an amyloid PET imaging GWAS (N = 7,036, 35% amyloid positive, 53.67% female, age = 71) and an autopsy GWAS of brain amyloidosis (N = 6,519, 63.08% amyloid positive, 51.34% female, age at death = 83), both adjusted for covariates including age, sex and principal components of genetic ancestry. All participants in both GWAS were unrelated individuals of European descent. We defined amyloid positivity as moderate or frequent neuritic plaques using the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) staging scores within each autopsy cohort. A Gaussian mixture model (GMM) was applied to each amyloid PET imaging cohort to identify the cohort and tracer‐specific cut‐offs that differentiate amyloid positive and negative populations. Result: The genome‐wide significant results from the meta‐analysis identified three known AD loci (APOE, CR1, and BIN1) and a novel chromosome 17 locus (rs35635959, intergenic, MAF = 0.27, OR = 1.18, p = 1.47 × 10‐8). The MAGMA gene‐level analysis excluding the APOE region suggests significant associations between rs35635959 and COASY, PLEKHH3, and TUBG2 on chromosome 17, implying the potential roles of these genes on amyloidosis. Functional annotations of rs35635959 leveraging brain eQTL databases indicate its association with differential gene expression of COASY and TUBG2 in AD brains. ROSMAP bulk and single‐nucleus RNAseq analyses link TUBG2 downregulation to the higher amyloid burden and AD diagnosis while suggesting such observations are enriched in excitatory and inhibitory neurons, reaching FDR significance. Conclusion: This study is the most extensive European GWAS of brain amyloidosis, and our findings replicate known AD loci and identify a possible novel locus (index SNP rs35635959) on chromosome 17. Functional annotations of the novel variant indicate TUBG2, implicating in microtubule organization, warrants further assessment. Ongoing efforts aim to validate these novel effects using independent datasets. [ABSTRACT FROM AUTHOR]

Background: The relationship between Alzheimer's disease (AD) pathology and the associated clinical syndrome a patient presents with remains indeterminate. Cognitively‐defined subgroups of AD have revealed distinctions based on relative cognitive impairments, including AD‐Language, where challenges in language are substantial, and AD‐No Domain, where no relative asymmetries across cognitive domains occur. Pathological features of AD have been associated as the primary neuropathology of the logopenic variant of primary progressive aphasia (lvPPA). Hallmark clinical features of lvPPA include relatively spared comprehension in the face of decline in naming and repetition abilities. This work aimed to test the hypothesis that the lvPPA language profile was overrepresented in AD‐Language when compared to AD‐No Domain. Method: Measures of verbal comprehension, confrontation naming, and phrase‐level repetition were obtained from all participants from the Religious Orders Study (ROS), the RUSH Memory and Aging Project (MAP) and the Minority Aging Research Study (MARS) using confirmatory factor analyses. We subsetted the data to include participants belonging to the AD‐Language and AD‐No Domain groups at their initial AD diagnosis visit. We compared patterns of language profiles based on strengths and weaknesses in comprehension, naming, and repetition. Pearson's Chi‐squared tests with Yates continuity correction was used to test if the language patterns were statistically different between the two AD subgroups. Results: We analyzed language performance in 642 participants across AD‐Language (31.8%) and AD‐No Domain (68.2%) groups (Table 1). Thresholds were based on AD‐No Domain and set as the median for each subdomain (comprehension = ‐.101, naming = ‐.957, repetition =.233) to establish whether a score represented a relative strength or weakness in the language profile. Eight patterns of language profiles based on strengths and weaknesses in comprehension, naming, and repetition were formed (Figure 1). The distribution of language patterns differed significantly between AD‐Language and AD‐No Domain (χ2 = 97.6, p <.001). Furthermore, the lvPPA pattern was found more frequently in AD‐Language (χ2 = 28.1, p <.001). Conclusion: Heterogeneity within the AD‐Language spectrum includes a significant proportion that is consistent with the language profile of lvPPA. Relative performance in domains of verbal comprehension, confrontation naming, and phrase‐level repetition varied by AD subgroup. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Alzheimer's disease (AD) pathology is defined by β‐amyloid (Aβ) plaques and neurofibrillary tau, but Lewy bodies (LBs; 훼‐synuclein aggregates) are a common co‐pathology for which effective biomarkers are needed. METHODS: A validated α‐synuclein Seed Amplification Assay (SAA) was used on recent cerebrospinal fluid (CSF) samples from 1638 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, 78 with LB‐pathology confirmation at autopsy. We compared SAA outcomes with neuropathology, Aβ and tau biomarkers, risk‐factors, genetics, and cognitive trajectories. RESULTS: SAA showed 79% sensitivity and 97% specificity for LB pathology, with superior performance in identifying neocortical (100%) compared to limbic (57%) and amygdala‐predominant (60%) LB‐pathology. SAA+ rate was 22%, increasing with disease stage and age. Higher Aβ burden but lower CSF p‐tau181 associated with higher SAA+ rates, especially in dementia. SAA+ affected cognitive impairment in MCI and Early‐AD who were already AD biomarker positive. DISCUSSION: SAA is a sensitive, specific marker for LB‐pathology. Its increase in prevalence with age and AD stages, and its association with AD biomarkers, highlights the clinical importance of α‐synuclein co‐pathology in understanding AD's nature and progression. Highlights: SAA shows 79% sensitivity, 97% specificity for LB‐pathology detection in AD.SAA positivity prevalence increases with disease stage and age.Higher Aβ burden, lower CSF p‐tau181 linked with higher SAA+ rates in dementia.SAA+ impacts cognitive impairment in early disease stages.Study underpins need for wider LB‐pathology screening in AD treatment. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging‐defined subgroups. METHODS: We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid‐negative controls. We used voxel‐based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD‐Memory. RESULTS: There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD‐Memory, lower left hemisphere GMV for AD‐Language, anterior lower GMV for AD‐Executive, and posterior lower GMV for AD‐Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD‐Language group than any other group (p = 1.15 × 10−10). For overlap between imaging‐defined and cognitively defined subgroups, AD‐Memory matched up with an imaging‐defined limbic predominant group. DISCUSSION: MRI findings differ across cognitively defined AD subgroups. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Social connectedness is associated with slower cognitive decline among older adults. Recent research suggests that distinct aspects of social networks may have differential effects on cognitive resilience, but few studies analyze brain structure. METHODS: This study includes 117 cognitively impaired and 59 unimpaired older adults. The effects of social network characteristics (bridging/bonding) on brain regions of interests were analyzed using linear regressions and voxel‐wise multiple linear regressions of gray matter density. RESULTS: Increased social bridging was associated with greater bilateral amygdala volume and insular thickness, and left frontal lobe thickness, putamen, and thalamic volumes. Increased social bonding was associated with greater bilateral medial orbitofrontal and caudal anterior cingulate thickness, as well as right frontal lobe thickness, putamen, and amygdala volumes. DISCUSSION: The associations between social connectedness and brain structure vary depending on the types of social enrichment accessible through social networks, suggesting that psychosocial interventions could mitigate neurodegeneration. Highlights: Distinct forms of social capital are uniquely linked to gray matter density (GMD).Bridging is associated with preserved GMD in limbic system structures.Bonding is associated with preserved GMD in frontal lobe regions.Bridging is associated with increased brain reserve in sensory processing regions.Bonding is associated with increased brain reserve in regions of stress modulation. [ABSTRACT FROM AUTHOR]

Paravascular cerebrospinal fluid (pCSF) surrounding the cerebral arteries within the glymphatic system is pulsatile and moves in synchrony with the pressure waves of the vessel wall. Whether such pulsatile pCSF can infer pulse wave propagation—a property tightly related to arterial stiffness—is unknown and has never been explored. Our recently developed imaging technique, dynamic diffusion‐weighted imaging (dynDWI), captures the pulsatile pCSF dynamics in vivo and can explore this question. In this work, we evaluated the time shifts between pCSF waves and finger pulse waves, where pCSF waves were measured by dynDWI and finger pulse waves were measured by the scanner's built‐in finger pulse oximeter. We hypothesized that the time shifts reflect brain‐finger pulse wave travel time and are sensitive to arterial stiffness. We applied the framework to 36 participants aged 18–82 years to study the age effect of travel time, as well as its associations with cognitive function within the older participants (N = 15, age > 60 years). Our results revealed a strong and consistent correlation between pCSF pulse and finger pulse (mean CorrCoeff = 0.66), supporting arterial pulsation as a major driver for pCSF dynamics. The time delay between pCSF and finger pulses (TimeDelay) was significantly lower (i.e., faster pulse propagation) with advanced age (Pearson's r = −0.44, p = 0.007). Shorter TimeDelay was further associated with worse cognitive function in the older participants. Overall, our study demonstrated pCSF as a viable pathway for measuring intracranial pulses and encouraged future studies to investigate its relevance with cerebrovascular functions. A novel framework was proposed to measure the brain‐finger pulse wave propagation through imaging the paravascular fluid dynamics. The brain‐finger TimeDelay was found to be significantly associated with age and neurocognition. Our results suggest that paravascular fluid is a viable pathway for measuring intracranial pulse waves and inferring arterial stiffness. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Our previously developed blood‐based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain‐based TRS. METHODS: We integrated AD genome‐wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N = 878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). RESULTS: Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. DISCUSSION: Our results suggest brain‐based TRS improves the AD classification of PRS and may be a potential AD biomarker. Highlights: Transcriptional risk score (TRS) is developed using brain RNA‐Seq data.Higher TRS values are shown in Alzheimer's disease (AD).TRS improves the AD classification power of PRS up to 16%.TRS is associated with AD pathology presence.TRS is associated with worse cognitive performance and faster cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Social connectedness is associated with better cognitive performance among older adults. Previously, we found that bridging (access to an expansive network of diverse and loosely connected individuals) and bonding (close and supportive relationships to familiar others) associated with preserved gray matter density (GMD). We sought to examine whether sex modulates these relationships in the Social Networks and Alzheimer's Disease study (SNAD). Method: We analyzed baseline data of 176 SNAD participants (58 male, 118 female; 117 cognitively‐unimpaired, 59 cognitively‐impaired; average age 68.57+/‐12.26 years). Bridging and bonding metrics were collected via in‐person interviews. We ran analyses in the pooled sample split by sex. Demographics and biomarkers between sexes were compared. Correlations between MRI regions of interest and bridging and bonding in males and females were performed. Voxel‐wise covaried for age, education, and intracranial volume in SPM12 yielded statistical maps for males and females which were corrected for family‐wise errors at p<0.01. Result: Table 1 shows demographic and biomarker comparisons between males and females. In females greater social bridging was significantly associated with (left p = 0.004, right p<0.001), as well as left frontal lobe thickness (p = 0.002), putamen (p = 0.011), and thalamus volumes (p = 0.002). No significant associations were observed for social bridging in males. Greater social bonding was significantly associated with preserved right frontal pole (p = 0.018) and caudal anterior cingulate thickness (p = 0.017) in females and greater right orbitofrontal thickness (p<0.001) in males (Figure 1). Our 3D voxel‐wise regression maps showed that in females greater social bridging was associated with preserved GMD of the frontal, temporal and parietooccipital lobes, while males showed no significant associations (Figure 2). In females greater social bonding was associated with preserved GMD in the lateral temporal and parietal lobes, while in males the associations were localized to the medial frontal and right orbitofrontal lobe (Figure 2). Conclusion: Women compared to men showed stronger effects of social enrichment on GMD and subcortical structures. Social bridging and bonding were associated to different brain regions in males and females. These results suggest that females may have a greater social‐based protection against neurodegeneration compared to their male counterparts. [ABSTRACT FROM AUTHOR]

Background: The eye often reflects changes seen in the brain in neurodegenerative diseases. This study sought to examine the relationship of contrast sensitivity measured using frequency doubling technology (FDT) and retinal thickness measured using optical coherence tomography (OCT) with temporal lobe neurodegeneration, cerebral tau deposition, and mean plasma ptau181 in older adults along the Alzheimer's disease (AD) continuum. Method: Participants included 28 individuals with subjective cognitive decline (SCD), 18 mild cognitive impairment (MCI), 4 with AD, and 30 age‐matched cognitive normal (CN) from the Indiana Memory and Aging Study at the Indiana ADRC. Participants were excluded from the study if they had significant eye disease determined to interfere with OCT, FDT, non‐AD dementia, or exclusion for MRI or PET. Mean deviation (MD) and pattern standard deviation (PSD) from FDT were used as measures of contrast sensitivity. OCT scans from each eye to measure retinal thickness in the retinal nerve fiber layer (RNFL) and macula. MRI scans were processed using Freesurfer v6 to measure medial (MTL) and lateral temporal lobe (LTL) volumes. LTL SUVR values were extracted from [18F]flortaucipir PET scans. Plasma ptau 181 was measured using a SIMOA assay. Finally, the association between contrast sensitivity, average RNFL thickness, macular volumes with temporal lobe volumes, LTL tau, and mean plasma ptau181 was assessed using a partial Pearson correlation, covaried for age, sex, and diagnosis. p<0.05 was considered significant. Result: RNFL thickness was decreased in patients with AD. Hippocampal volume correlated with RNFL thickness and macular volume in both eyes and with PSD in the left eye. The inferior temporal volume correlated with MD in both eyes, while mean plasma ptau181 with PSD in both eyes. Finally, lateral temporal lobe tau was correlated with the inner retinal layers of ganglion cell layer plus inner plexiform layer thickness. Conclusion: Contrast sensitivity correlates with temporal lobe volumes and mean plasma ptau181. RNFL thinning and macular volumes are associated with hippocampal atrophy, and inner retinal nuclear layers correlate with lateral temporal tau. Imaging of the eye may represent a useful biomarker to screen patients at risk for AD prior to more invasive and prolonged testing. [ABSTRACT FROM AUTHOR]

Und: Alzheimer's disease (AD) is characterized by cognitive dysfunction likely secondary to progressive neurodegeneration in the brain. However, to what extent regional cortical thickness (CT) changes are related to cognitive decline in the Asian population remains unclear. Method: We measured CT using Freesurfer v6 from T1‐weighted magnetic resonance imaging (MRI) data in 603 Korean participants, including 71 young and 288 old cognitively unimpaired (CU young and CU old), 149 with mild cognitive impairment (MCI), and 92 AD from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE). Cognitive function of each participant was quantified by the MMSE. MRI and cognitive data were acquired at baseline (n = 603) and a 2‐year follow‐up visit (n = 380). Linear mixed‐effect models were used to estimate diagnosis group, time, and group‐by‐time interaction effects on CT measurements, with age, sex, and education as covariates. Pearson's correlations between CT measurements and MMSE total score were computed, corrected for multiple comparisons by false discovery rate (FDR). Result: At baseline, CU old participants had lower CT values than CU young participants in the inferior parietal cortex and frontal and parietal regions. In comparison with CU old participants, MCI and AD showed widespread lower CT values, more pronounced in the medial temporal lobe (i.e., the entorhinal cortex and parahippocampal gyrus), inferior temporal cortex, medial prefrontal cortex, and medial parietal cortex (i.e., the posterior cingulate cortex and precuneus), with AD lower than MCI in these regions. These regions also showed the most longitudinal atrophy in both AD and MCI over 2 years. AD patients showed faster and more widespread atrophy patterns than MCI and CU old participants. We identified significant (PFDR<0.05) positive linear correlations between cognitive decline and regional CT measurements, particularly in regions showing significant cortical thinning across time. Conclusion: In this study, we replicated the typical cross‐sectional and longitudinal brain atrophy patterns, and relationships with cognitive decline in an Asian cohort. Our findings support the generalizability of atrophy patterns in MCI and AD, suggesting common neurobiological mechanisms underlying AD symptoms across different ethnoracial populations. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) progresses along a continuum and begins many years before symptom onset. AD takes a long progression time, making it hard to study the entire spectrum of AD development. Pseudotime analysis widely used in cell lineage analysis have been applied to AD gene expression data to understand the molecular changes along progression. In this work, we performed a neuroimaging‐based pseudotime analysis to explore the progression patterns of different imaging modalities. Method: Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). Data from three imaging modalities were collected including MRI (N = 1684), Amyloid (N = 1041), and Tau (N = 540) (Table.1). Out of those, 223 subjects have all three modalities. We tested four different pseudotime analysis tools: PHATE, MONOCLE3, Slingshot, and Destiny. MRI, Amyloid, Tau, and the combined multi‐modality data were used to estimate the pseudotime respectively. Only PHATE returned non‐reversed pseudotime and clear disease trajectories Result: Amyloid derived pseudotime best captures the continuum of disease progression and differentiate diagnosis groups the best (Fig.1(b,f)). It also showed better correlation with clinical cognition scores than the composite amyloid measure (Fig.2(b,d)). Multi‐modality derived pseudotime reveals turning point of Amyloid and Tau. We found that the accumulation of Amyloid starts to accelerate when the composite SUVR reaches ∼1.2, which agrees well with existing findings (Fig.2(g,h)). Such turning point does not exist for amyloid derived pseudotime. It also reveals temporal order of brain changes within and across modalities (Fig.2(e,f)), where we observed overall amyloid changes starts early (i.e., small pseudotime) and accelerates earlier than overall brain atrophy and Tau deposition. With a closer look into Tau regions, we found Tau deposition in BRAAK 1 regions accumulates in a faster rate than those inBRAAK34 and BRAAK56 regions. Conclusion: Pseudotime analysis has a great potential for us to better understand the progression of Alzheimer's disease. New approaches to fully leverage the longitudinal imaging data could provide a refined view of imaging changes and their co‐occurance pattern along AD progression. Longitudinal data in pseudotime analysis could also help capture the subtle changes in very early stage and therefore would likely provide some valuable insights to improve early diagnosis. [ABSTRACT FROM AUTHOR]

Background: The brain‐age algorithm utilizes a regression model of the typical aging trajectory, trained on structural MRIs and chronological ages of a large population of healthy individuals spanning a wide age range (Cole et al. 2017, Neuroimage). MRIs from new populations are then compared to this regression for estimating brain age, a biomarker of brain health. Few studies have investigated stability of the brain‐age model across field strength and the influence of voxel size and race is understudied. Method: We analyzed stability of the brain‐age model within and across field strength in the ADNI cohort (n = 75: 25 CN/25 MCI/25 AD) using intra‐class correlation coefficients (ICC) and multiple R2 values. Impact of voxel size (0.8mm, 1mm, and ADNI2 sequence 1.1×1.1×1.2mm) was compared in a sample from the Indiana ADRC (n = 48: 19 CN/16 SCD/10 MCI/3 AD). We then calculated brain age gap (BAG; estimated– chronological age) and compared results across self‐reported racial and ethnic groups matched on diagnosis, sex, age, education, and handedness using independent t‐tests. BAG results of African Americans (AAs) from the IADRC (n = 71: 30 CN/19 SCD/15 MCI/7 AD) and ADNI (n = 147: 41 CN/20 EMCI/27 LMCI/15 AD/44 SMC), and Hispanics (n = 76: 24 CN/16 EMCI/11 LMCI/11 AD/14 SMC) and Asians (n = 47: 12 CN/7 EMCI/4 LMCI/9 AD/15 SMC) from ADNI only, were compared to matched non‐Hispanic Whites (NHWs). Result: The brain‐age method was stable across repeated scans within field strength, but less stable across field strengths (Fig.1). 3T scans consistently estimated individuals to be ∼4.43 years younger than the 1.5T scans. The method was relatively stable within and across voxel sizes (Fig.2). There was a significant difference in BAG between races (NHWs and AAs, and NHWs and Asians), but not between ethnicities (NHWs and Hispanics) (Fig.3). Conclusion: Due to the model's variability across field strength, we propose a linear offset equation for data harmonization (Fig.1): ((3T estimate/0.92)+9.55). We are also investigating ComBat harmonization to eliminate effects of field strength. While other studies show structural differences across race, it will be important to determine whether the self‐reported racial differences in BAG are due to inherent model bias or true biological differences. [ABSTRACT FROM AUTHOR]

Background: Diffusion tensor imaging (DTI) measures water diffusion within brain tissue and can provide information regarding white matter, myelin, and axonal degeneration. We analyzed the cingulum bundle of the hippocampus (CBH), chosen for relevance to Alzheimer's Disease (AD) and memory (Wen et al, 2019; Dalboni da Rocha et al, 2020), using fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AxD), and mean diffusivity (MD), as well as their relationship to cognition, measured by DTI data from the initial phase of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE). Methods: Participants from KBASE included cognitively normal older adults (CN, n = 276), mild cognitive impairment (MCI, n = 142), and AD dementia (AD, n = 87) studied with baseline neuroimaging and cognitive testing at Seoul National University and processed at Indiana University School of Medicine. Affine and non‐linear transformations were used to align individual FA maps to the FMRIB58FA template (FSL). Voxel values were extracted from unilateral CBH regions (determined using JHU ICBM‐DTI‐81‐white‐matter‐labels atlas) and averaged. Univariate‐GLM and Bonferroni‐corrected post‐hoc T‐tests controlling for age and sex were used to analyze diagnostic group differences, with lower FA and higher RD, AxD, and MD values expected in MCI and AD groups compared to CN. Bonferroni‐adjusted partial correlations controlling for age, sex, and education were used to test the hypothesis that DTI values would be associated with worsening cognitive scores. Results: As hypothesized, bilateral CBH DTI values in AD and MCI were significantly worse than those in CN (p<0.001) (Fig 1) with moderate effect sizes (η2 = 0.111 – 0.307). Worsening DTI values were significantly associated with worsening Digit Span Backward, Trailmaking A and B, GDS, MMSE‐KC, CDR‐SB, Word List Recall, and Logical Memory scores at the Bonferroni‐adjusted p‐value of 0.003. Bilaterally, higher Digit Span Forward scores were significantly associated with AxD values and lower Total SMCQ scores were associated with RD, AxD, and MD values. Conclusion: Within the KBASE cohort, MCI and AD demonstrated significantly worse DTI, memory, and cognition consistent with the pattern of white matter microstructural damage previously reported in MCI and AD (Wen et al, 2019; LoBuono et al., 2019). [ABSTRACT FROM AUTHOR]

Background: Multi‐omic data like genotype, gene expression and protein expression have been increasingly explored in post‐GWAS era to interpret GWAS findings and to gain more insight of the Alzheimer's disease (AD) mechanism. However, each ‐omics data type is usually examined individually and identified genetic variations, genes and proteins are not necessarily functionally related. Method: We used GWAS genotype, RNA‐Seq gene expression, and protein expression data from 133 subjects of ROS/MAP Project for original study and 121 subjects of Mount Sinai Brain Bank (MSBB) for replication (Fig. 1a). The multi‐omic data pre‐processing steps were shown in Fig. 1b. We proposed a new interpretable deep neural network MoFNet, extended from Varmole [1], to jointly model the prior knowledge of functional interactions using ROS/MAP cohort (Fig. 1c). It aimed to identify a subnetwork of functional interactions from prior knowledge that are both predictive of AD and evidenced by multi‐omic data. Particularly, prior functional interaction network was embedded into the architecture of MoFNet in a way that it resembles the information flow from DNA to gene and protein. Result: The proposed model MoFNet significantly outperformed all other state‐of‐art classifiers when evaluated using multi‐omic data from ROS/MAP cohort (Table 1). It yielded three major multi‐omic sub‐networks related to innate immune system, clearance of mis‐folded proteins, and neurotransmitter release respectively (Fig. 2a). One subnetwork includes all the major members of the SNARE complex, an essential mediator of synaptic vesicle fusion. Majority of our identified genes/proteins are related to synaptic activities in neuronal, astrocyte and microglial cells. Differential expression analysis show MoFNet returned more genes and proteins due to functional connections (Fig. 2b). Around 50% of these findings were replicated in another independent cohort (MSBB). Conclusion: Our identified gene/proteins are highly related to synaptic vesicle function. Altered regulation or expression of these genes/proteins could cause disruption in neuron‐neuron or neuron‐glia cross talk and further lead to neuronal and synapse loss in AD. Further investigation of these identified genes/proteins could possibly help decipher the mechanisms underlying synaptic dysfunction in AD, and ultimately inform therapeutic strategies to modify AD progression in the early stage. [1] Nguyen et al., Bioinformatics, 2021. [ABSTRACT FROM AUTHOR]

BACKGROUND: Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre‐symptomatic risk assessment but also for building personalized therapeutic strategies. METHODS: We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD‐risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed. RESULTS: Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD‐risk SNPs were significant predictors of AD progression. DISCUSSION: The model successfully estimated the contribution of AD‐risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine. [ABSTRACT FROM AUTHOR]

Background: Dementia is age‐related with a significant genetic contribution, yet genome‐wide association studies have not fully accounted for heritability. This discrepancy may in part be due to reliance on SNPs and small indels. Whole‐genome sequencing (WGS) data in the Japanese population may reveal population‐specific susceptibility loci for dementia. Retinal imaging with optical coherence tomography (OCT) is noninvasive, reproducible, and can detect thinning associated with progressive neurodegeneration. Association of population‐specific genetic susceptibility loci with retinal thinning and cognitive decline may reveal novel aspects of dementia risk and pathophysiology. Method: Among participants with WGS data from the Tohoku Medical Megabank Organization (ToMMo) Ophthalmology Study ("ToMMo Eye Study"), individuals with adequate quality data on retinal nerve fiber layer and ganglion cell layer thickness from spectral‐domain optical coherence tomography (SD‐OCT) scans were selected. Since retinal thinning also occurs in glaucoma, we performed a GWAS using age, sex, and 10 principal components as covariates using SAGE1.2 to obtain a set of genes responsible for glaucoma and confirm that the genotyping was successful. We then attempted to identify susceptibility loci for cognitive decline by using (1) the Mini‐Mental State Examination, Japanese version (MMSE‐J), (2) the Montreal Cognitive Assessment, Japanese version (MoCA‐J), and (3) the Mini‐COG© (a simple screening for early detection of dementia, Japanese version) scores as associated factors, respectively. Furthermore, these validation results were also compared with those obtained from GWAS using imputation data performed on custom arrays (Japonica ArrayTM, v2 or NEO) for Japanese. Result: 84 significant (p < 5.0E‐8) genome‐wide susceptibility loci (hg38) of RNFL were detected on 14K WGS‐based study (the top hit locus: Chr14, SIX6 gene, P = 4.50E‐46). There were many genetic loci that have already been reported to be associated with glaucoma susceptibility, including the above locus. Among the results of GWAS for cognitive decline combining the three cognitive scores after normalization to z‐scores, several loci have shown significant susceptibility in both of RNFL and cognitive rating scale. Some loci suggested more than a high or moderate effect of altering protein efficacy. Conclusion: We present an initial WGS‐based genetic study of retinal thickness and cognitive decline in the Japanese population. [ABSTRACT FROM AUTHOR]

Background: Large‐scale genome‐wide association studies (GWAS) of Alzheimer's disease (AD) from European ancestry identified many genetic variants associated with clinical diagnosis of AD dementia. However, it remains unclear whether these AD‐related variants are associated with AD biomarkers, particularly hippocampal atrophy, a well‐known neurodegeneration biomarker of AD in a Korean population. In this study, we investigated the association between known AD risk single nucleotide polymorphisms (SNPs) and hippocampal atrophy along the AD continuum in older Korean adults. Method: A total of 487 participants (258 cognitively normal olde adults [CN], 144 mild cognitive impairment [MCI], 85 AD dementia) from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) were included for analysis. All participants underwent 11C‐PiB‐PET/MRI. Hippocampal volume adjusted for intracranial volume (HVa) was obtained from 3D T1‐weighted MRI scans using FreeSurfer and used as a neurodegeneration marker of AD. Global beta‐amyloid (Aβ) deposition was calculated from PiB uptake in the global cortical region‐of‐interest using SPM12. From the genetic evidence gathered by the AD Sequencing Project (ADSP), which consists of 76 SNPs associated with AD, we selected 38 SNPs with a minor allele frequency (MAF) greater than 1% from the genotyping data imputed using the TOPMed imputation server in the KBASE cohort. Result: Among 38 known AD‐related SNPs, three SNPs (rs6966331 in EPDR1, rs2242595 in MYO15A, and rs17125924 in FERMT2) were associated with HVa in an initial exploratory analysis (p<0.05). In a subsequent confirmatory analysis, the associations of rs6966331 in EPDR1 and rs2242595 in MYO15A with HVa remained significant after controlling for age, sex, and APOE4 carrier status, as well as global Aβ deposition (p<0.001 and p = 0.009 for rs6966331 and rs2242595, respectively) (Table 1). Conclusion: Our study identified associations of rs6966331 in EPDR1 and rs2242595 in MYO15A with hippocampal volume in Korean older adults, and these associations were independent of cerebral Aβ deposition and APOE4 carrier status. These findings suggest that these AD‐related loci may contribute to the development of AD dementia via Aβ‐independent neurodegeneration. [ABSTRACT FROM AUTHOR]

Background: Systemic inflammation plays a pivotal role in many chronic diseases including Alzheimer's disease (AD). Assessing the composition of immune pathways in neurodegenerative diseases can contribute to precision medicine. Using publicly available transcriptomic data, we sought to elucidate transcriptional networks pertinent to inflammatory pathways across brain regions and peripheral blood in AD/mild cognitive impairment (MCI) and peripheral blood in Parkinson's disease (PD). Method: For the AD/MCI vs. control dataset, we analyzed bulk‐RNAseq collected from 6 brain regions of donors from ROSMAP, Mayo Clinic, and Mount Sinai School of Medicine (MSSM) brain banks available from the AMP‐AD consortium. Ante‐mortem, blood RNAseq expression data was retrieved from the AMP‐AD Emory Vascular cohort and Mayo Clinic Study of Aging (MCSA). We also collected blood‐derived microarray expression data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For the PD vs Control dataset, blood‐derived bulk‐RNAseq from the PDBP and PPMI cohorts were available through the AMP‐PD consortium. Following quality control, normalization, and residual generation to account for biological and technical variables, co‐expression network modules and their enriched pathways were identified using WGCNA within each dataset. Module/trait correlation tests for aging and diagnosis (cases [AD/MCI or PD] vs control) phenotypes were evaluated. Gene Ontology enrichment analyses were executed to identify enriched pathways and brain or blood cell types within the modules. Modules were tested for preservation across cohorts. Result: We identified conserved immune signatures across brain regions and cohorts. Modules involved in immune response were preserved across all cohorts. Blood consensus modules involved in immune response were preserved in the brain and vice versa. Some immune modules were associated with AD/MCI, PD, and/or aging. Brain immune modules are significantly associated with aging and/or AD. Significant correlations (q<0.05) with PD diagnosis were present. In the MCSA and Emory vascular cohorts, there were no significant (q<0.05) associations between modules and diagnosis, while in ADNI there were nominal (p<0.05) associations. Conclusion: Preserved transcriptional immune networks were identified across blood and brain and across two neurodegenerative diseases. Expanding gene co‐expression network analyses to other diseases and integrating additional omics measures and phenotypes can further strengthen these findings to unravel the immune signatures across complex diseases. [ABSTRACT FROM AUTHOR]

Background: Genome‐wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer's disease (AD) risk, but genetic variation in the onset and progression of AD pathology is less understood. Accumulation of amyloid‐β (Aβ) in the brain is a key pathological hallmark of AD beginning 10 – 20 years prior to cognitive symptoms. We investigated the genetic basis of variation in age at onset (AAO) of brain Aβ by comparing the performance of polygenic scores (PGSs) based on AD risk and resilience with a Aβ‐AAO trait‐specific PGS. Method: 1122 participants from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study underwent genome‐wide SNP genotyping and assessment of brain Aβ using positron emission tomography (PET) imaging at two or more timepoints. AAO was the age at which participants were estimated to have crossed the 20 centiloid (CL) threshold for high Aβ. We utilised AD risk and resilience GWAS summary statistics and conducted a GWAS for AAO using a cross‐validation approach (10 test‐validation folds). We used PRSice to identify optimal PGSs for Aβ‐AAO for risk (PGSRisk), resilience (PGSResilience) and Aβ‐AAO (PGSAAO). Result: PGSRisk and PGSResilience were both significantly associated with Aβ‐AAO, such that higher PGSRisk and lower PGSResilience were associated with an earlier Aβ‐AAO. PGSRisk showed the strongest association and explained more variance in Aβ‐AAO than did PGSAAO. When stratified by APOE ε4 carriage, the strongest genetic risk factor for AD, the association of PGSRisk with Aβ‐AAO was stronger among ε4 non‐carriers, whilst PGSResilience, was more strongly associated with Aβ‐AAO in ε4 carriers. Conclusion: PGS based on genetic risk and resilience for AD are both significant predictors of the age at which people are estimated to cross the threshold for high brain Aβ burden. Predicting the age at which a person will pass this threshold would enable treatment at an earlier stage, when it may more effectively delay or prevent symptom onset. [ABSTRACT FROM AUTHOR]

Background: Non‐coding RNA species, such as microRNA (miRNA), regulate multiple biological and pathological processes by binding to target mRNAs and facilitating alteration of translation levels via complexes such as RNA‐induced silencing complex (RISC). Disrupting this process could contribute to AD pathogenesis by fostering aggregation of hyperphosphorylated microtubule‐associated protein tau and amyloid‐β (Aβ) peptides, and neuroinflammation. Understanding how these pathological changes are regulated remains our research focus. We report that miR298 plays a vital role in maintaining APP and tau homeostasis and that miR298 imbalances may impact AD progression. Method: Levels of miR298 from non‐cognitively impaired (NCI) and AD subject brain tissue samples from different recognized sources were measured by qRT‐PCR and assessed for associations with AD risk and potential covariates such as age and APOE genotype. Other biomarkers were assessed in cortical samples from the same subjects, as we previously described. Further, APP, tau, and cytokines were profiled in miR298 mimic‐ or its antagomiR‐expressing human neuronal and astrocyte cultures. Result: Levels of miR298 varied in postmortem temporal lobe between AD patients and age‐matched NCI controls. Higher brain miR298 levels were associated with a reduced risk of AD. Subject age and APOE genotype altered this association; specifically, greater age and dose of the APOEε4 allele were associated with an increased risk of AD. However, APOEε4 dose‐associated risk reduced as age increased. We identified putative binding sites for miR298 on APP, BACE1, MAPT, IL1α, and IL6 mRNAs to form RISC. We showed that treatment by miR298 reduced tau, APP, and BACE1 proteins and mRNA levels in cell cultures. Conclusion: These studies suggest that miR298 regulates a coordinated network of AD‐related proteins APP, BACE1, and tau. Hence, such network regulation may represent a rational therapeutic target for reducing AD risk and disease modification. In addition to late‐onset cases, we will profile miR298 in brain tissue samples from early‐onset AD cases. Future work involves testing miR298 in AD animal models, such as in human tau‐overexpressing transgenic mice. We sincerely thank grant support from NIA/NIH. [ABSTRACT FROM AUTHOR]

Background: Despite significant advancements in the development of blood biomarkers for AD, challenges persist due to the complex interplay of genetic and environmental risk factors in AD pathogenesis. Epigenetic processes, including non‐coding RNAs and especially microRNAs (miRs), have emerged as important players in the molecular mechanisms underlying neurodegenerative diseases. MiRs have the ability to fine‐tune gene expression and proteostasis, and microRNAome profiling in liquid biopsies is gaining increasing interest since changes in miR levels can indicate the presence of multiple pathologies. We have profiled blood samples via smallRNA sequencing for 1056 individuals of the DELCODE and 847 individuals of the ANDI cohort. Methods: We profiled blood samples via smallRNA sequencing for 1056 individuals of the DELCODE (German Longitudinal Cognitive Impairment and Dementia Study) and 847 individuals of the ANDI (Aging and Dementia in the Community) cohort, consisting of individuals diagnosed with SCD, MCI, AD, or control. Results: By applying differential expression, WGCNA, as well as linear and non‐linear machine learning approaches, we identify microRNA signatures that can help identify patients at distinct stages of disease progression, as well as signatures that can predict the course of the disease. These data are compared with phenotyping data, such as cognitive function and ATN biomarkers. We will also discuss the role of other non‐coding RNAs besides microRNAs and provide a framework for developing RNA‐based point‐of‐care assays. [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer's disease (AD) is heterogeneous, both clinically and neuropathologically. We investigated whether polygenic risk scores (PRSs) integrated with transcriptome profiles from AD brains can explain AD clinical heterogeneity. Methods: We conducted co‐expression network analysis and identified gene sets (modules) that were preserved in three AD transcriptome datasets and associated with AD‐related neuropathological traits including neuritic plaques (NPs) and neurofibrillary tangles (NFTs). We computed the module‐based PRSs (mbPRSs) for each module and tested associations with mbPRSs for cognitive test scores, cognitively defined AD subgroups, and brain imaging data. Results: Of the modules significantly associated with NPs and/or NFTs, the mbPRSs from two modules (M6 and M9) showed distinct associations with language and visuospatial functioning, respectively. They matched clinical subtypes and brain atrophy at specific regions. Discussion: Our findings demonstrate that polygenic profiling based on co‐expressed gene sets can explain heterogeneity in AD patients, enabling genetically informed patient stratification and precision medicine in AD. HIGHLIGHTS: Co‐expression gene‐network analysis in Alzheimer's disease (AD) brains identified gene sets (modules) associated with AD heterogeneity.AD‐associated modules were selected when genes in each module were enriched for neuritic plaques and neurofibrillary tangles.Polygenic risk scores from two selected modules were linked to the matching cognitively defined AD subgroups (language and visuospatial subgroups).Polygenic risk scores from the two modules were associated with cognitive performance in language and visuospatial domains and the associations were confirmed in regional‐specific brain atrophy data. [ABSTRACT FROM AUTHOR]

Introduction: It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. Methods: Diffusion MRI data from several well‐established longitudinal cohorts of aging (Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], Vanderbilt Memory & Aging Project [VMAP]) were free‐water corrected and harmonized. This dataset included 1723 participants (age at baseline: 72.8 ± 8.87 years, 49.5% male) and 4605 imaging sessions (follow‐up time: 2.97 ± 2.09 years, follow‐up range: 1–13 years, mean number of visits: 4.42 ± 1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. Results: While we found a global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. Conclusions: There is a prevalent role of white matter microstructural decline in aging, and future large‐scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS: Longitudinal data were free‐water corrected and harmonized.Global effects of white matter decline were seen in normal and abnormal aging.The free‐water metric was most vulnerable to abnormal aging.Cingulum free‐water was the most vulnerable to abnormal aging. [ABSTRACT FROM AUTHOR]

Background: Cancer and its treatments may accelerate aging in survivors; however, research has not examined epigenetic markers of aging in longer term breast cancer survivors. This study examined whether older breast cancer survivors showed greater epigenetic aging than noncancer controls and whether epigenetic aging related to functional outcomes. Methods: Nonmetastatic breast cancer survivors (n = 89) enrolled prior to systemic therapy and frequency‐matched controls (n = 101) ages 62 to 84 years provided two blood samples to derive epigenetic aging measures (Horvath, Extrinsic Epigenetic Age [EEA], PhenoAge, GrimAge, Dunedin Pace of Aging) and completed cognitive (Functional Assessment of Cancer Therapy‐Cognitive Function) and physical (Medical Outcomes Study Short Form‐12) function assessments at approximately 24 to 36 and 60 months after enrollment. Mixed‐effects models tested survivor‐control differences in epigenetic aging, adjusting for age and comorbidities; models for functional outcomes also adjusted for racial group, site, and cognitive reserve. Results: Survivors were 1.04 to 2.22 years biologically older than controls on Horvath, EEA, GrimAge, and DunedinPACE measures (p =.001–.04) at approximately 24 to 36 months after enrollment. Survivors exposed to chemotherapy were 1.97 to 2.71 years older (p =.001–.04), and among this group, an older EEA related to worse self‐reported cognition (p =.047) relative to controls. An older epigenetic age related to worse physical function in all women (p <.001–.01). Survivors and controls showed similar epigenetic aging over time, but Black survivors showed accelerated aging over time relative to non‐Hispanic White survivors. Conclusion: Older breast cancer survivors, particularly those exposed to chemotherapy, showed greater epigenetic aging than controls that may relate to worse outcomes. If replicated, measurement of biological aging could complement geriatric assessments to guide cancer care for older women. Older breast cancer survivors were biologically older than matched noncancer controls across multiple epigenetic aging measures at 24 months or more after enrollment (which was presystemic therapy for survivors). Older breast cancer survivors who had received chemotherapy showed the greatest epigenetic aging, and among this group, an older epigenetic age was associated with worse self‐reported cognition relative to controls. [ABSTRACT FROM AUTHOR]

Background: Immune activation/inflammation markers (immune markers) were tested to explain differences in neurocognition among older breast cancer survivors versus noncancer controls. Methods: Women >60 years old with primary breast cancer (stages 0–III) (n = 400) were assessed before systemic therapy with frequency‐matched controls (n = 329) and followed annually to 60 months; blood was collected during annual assessments from 2016 to 2020. Neurocognition was measured by tests of attention, processing speed, and executive function (APE). Plasma levels of interleukin‐6 (IL‐6), IL‐8, IL‐10, tumor necrosis factor α (TNF‐α), and interferon γ were determined using multiplex testing. Mixed linear models were used to compare results of immune marker levels by survivor/control group by time and by controlling for age, racial/ethnic group, cognitive reserve, and study site. Covariate‐adjusted multilevel mediation analyses tested whether survivor/control group effects on cognition were explained by immune markers; secondary analyses examined the impact of additional covariates (e.g., comorbidity and obesity) on mediation effects. Results: Participants were aged 60–90 years (mean, 67.7 years). Most survivors had stage I (60.9%) estrogen receptor–positive tumors (87.6%). Survivors had significantly higher IL‐6 levels than controls before systemic therapy and at 12, 24, and 60 months (p ≤.001–.014) but there were no differences for other markers. Survivors had lower adjusted APE scores than controls (p <.05). Levels of IL‐6, IL‐10, and TNF‐α were related to APE, with IL‐6 explaining part of the relationship between survivor/control group and APE (p =.01). The magnitude of this mediation effect decreased but remained significant (p =.047) after the consideration of additional covariates. Conclusions: Older breast cancer survivors had worse long‐term neurocognitive performance than controls, and this relationship was explained in part by elevated IL‐6. The mechanisms that contribute to cognitive problems among cancer survivors remain unclear. This study found that one inflammatory/immune activation marker, interleukin‐6, mediated some of the relationship between older breast cancer survivors/noncancer control group and cognitive performance. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is highly heritable. Genome‐wide association studies and whole genome sequencing have uncovered many AD‐risk variants but are still falling short from providing an explanation of the full heritability. To address this issue, we devised a novel deep learning approach that can quantify the polygenic risk score (PRS) of genetic variants and their interactive effects at the individual level. Method: We applied a novel deep learning model to the 266,161 SNP chromosome 19 data of 313 AD and 457 cognitively unimpaired (CU) ADNI patients. Through occlusion maps, we quantified the predictive value of 70,643 possible AD‐determining SNPs and identified the top 0.05%, i.e., 35 SNPs, without imposing any additional pre‐assumptions. Epistatic effects increasing or decreasing the risk of AD were further investigated by using the co‐occlusion method. The top 35 AD‐determining SNPs were validated in an external dataset. Result: The model achieved 68.18% in predicting AD vs. CU. The top 35 AD SNPs showed dominant impact in AD prediction as single as well as an interactive factors. 574 interactions that increased AD risk were found for AD‐predicted patients while 358 interactions predictive of cognitively unimpaired performance were found for CU‐predicted patients. Rs56131196 (APOC1) was recognized as the most significant AD‐determining SNP. It showed interactions with at least 8 other SNPs that further influenced the risk of AD. Rs2229918 (EERC1) was recognized as the most powerful SNP influencing CU diagnostic accuracy. Conclusion: Our deep learning model provides a novel approach for assessing the genetic risk for AD at the individual chromosomal level. The results indicate that APOC1 and ERCC1 are strong genetic risk factors for the development of AD. These findings are consistent to the previous medical research. Our model's capability in deciphering the impact of SNPs on AD development could prove critical for early presymptomatic risk assessment and personalized therapeutic strategy approaches. [ABSTRACT FROM AUTHOR]

Background: Genome wide association study (GWAS) presented that a cohort having one or two copies of APOE4 show about 2.84 or 8.07 times, respectively, higher risk of having Alzheimer's disease (AD) than who do not. However, it is unclear how much impact does APOE4 have in progressing AD for an individual. We implemented a deep learning model to identify the APOE4's risk impact at the individual level. Method: We applied a novel deep learning model to the 266,161 SNP chromosome 19 data of 313 AD and 457 cognitively unimpaired (CU) ADNI patients. The whole dataset was divided into train, validation, and test set with a ratio of 60%, 20%, and 20% for model development. In the test set, 63.93% of AD participants were APOE4 carriers as opposed to 30.11% of CU participants. Therefore, APOE4 alone has a 67.5% diagnostic accuracy. Result: The model achieved 68.18% in classifying CU vs. AD and recognized SNPs in APOC1 (rs12721051, rs56131196, rs12721046, and rs4420638) as strong AD risk factors. On the other hand, SNPs in APOE did not show a risk impact such that removal or replacement of the SNPs of the APOE haplotype (rs429358 and rs7412) did neither increase nor decrease the likelihood of AD for any of the 154 test set participants. Furthermore, rs429358 and rs7412 did not show any interaction effects with other SNPs in chromosome 19. Further closer examination of these results confirmed that the APOE haplotype (rs429358 and rs7412) and the APOC1 SNPs (rs12721051, rs56131196, rs12721046, and rs4420638) are in strong linkage disequilibrium (normalized correlation coefficients range 0.685‐0.844) which explains why only one of the two genes was selected by our model. These results were reproduced by 5‐fold cross validation as well as with the implementation of a different deep learning model architecture. Conclusion: APOC1 variants were ranked higher and selected by our deep learning algorithm over the APOE haplotype that has been established as the strongest sporadic AD risk factor to date. APOC1 might be a major AD risk gene that needs further exploration. [ABSTRACT FROM AUTHOR]

Background: Several genetic variants in chromosome 19 have been determined as independent Alzheimer's disease (AD) risk factors. It is presumed that different combinations amongst them would further impact AD risk and progression at the individual level. In order to advance AD precision medicine, it is critical to understand the underlying complex genetic mechanisms. Deep learning technology is capable of not only identifying such variants but also defining complicated interactions that could further influence disease progression. Method: We applied a novel deep learning model to the 266,161 SNP chromosome 19 data of 313 AD and 457 cognitively unimpaired (CU) ADNI patients. By using occlusion mapping we identified the SNPs that contributed to predictive accuracy of AD vs. CU and sequentially replaced each of them with all other biologically plausible alleles to most precisely measure the impact of the variability at the SNP level. Next, we examined the possible epistatic interactions on the SNP level on AD likelihood by co‐occlusion of pairs of SNPs and investigating the additional changes on predictive accuracy. Result: Rs56131196 (APOC1) emerged as the most powerful chromosome 19 AD SNP. 79.69% (51/64) of the predicted AD patients were heterozygous, i.e., AG, at this locus. In contrast, 88.89% (80/90) of the predicted CU patients had GG genotype at rs56131196. Substituting AG for GG in predicted AD participants resulted in changing the diagnostic prediction to CU in 36% of the cases. Substituting GG for AA in predicted CU participants resulted in changing the diagnostic prediction to AD in 6% of the cases. The model further identified that AG→GG replacement further affects the likelihood of AD by interactions with SNPs in other genes including APOC1/APOC1P1, TOMM40, and ERCC1/CD3EAP. Conclusion: We tested the APOC1 rs56131196 genotype risk for AD by creating a simulator analogous to CRISPR. This innovative approach could provide a potential in silico CRISPR predictive modeling and help advance personalized preventive gene therapy for AD. [ABSTRACT FROM AUTHOR]

Background: KBASE is a prospective cohort study launched at Seoul National University (SNU), South Korea, in 2014 using similar design and methods as the North American Alzheimer's Disease Neuroimaging Initiative (ADNI). The KBASE cohort consists of well‐characterized participants (420 cognitively normal, 140 mild cognitive impairment, and 90 AD dementia) (Figure 1). It includes systematic longitudinal collection of comprehensive clinical, cognitive and lifestyle data, multimodal neuroimaging, and bio‐specimens for the first six years ("KBASE1") at a single center. "KBASE2: Korean Brain Aging Study, Longitudinal Endophenotypes and Systems Biology" (NIH Grant U01AG072177) is the second phase of KBASE, launched in collaboration with Indiana University and other institutions. KBASE2 began in 2021, and has similar assessment protocols to the KBASE1. The NIA AD Sequencing Project (ADSP) will perform GWAS and whole genome sequencing (WGS). Method: Neuropsychological data will be harmonized by the study team and members of the ADSP Phenotype Harmonization Consortium. Comprehensive cognitive data were collected using a battery of standardized tests (Table 1). Harmonization and co‐calibration will enable comparative analyses with cognitive data from similar studies with largely non‐Hispanic white (NHW) cohorts, including the ADNI and AIBL, and ultimately the range of other aging and dementia cohorts including those with underrepresented multi‐ethnic populations. WGS and GWAS data will be harmonized and shared by the ADSP. Result: Cognitive data harmonization processes are underway and have begun to address the challenges of language and cultural differences. Initial steps include mapping tests and items into an English template. Harmonization and co‐calibration efforts will include identifying appropriate anchor items and accounting for differences in test items that were made to be more culturally and linguistically appropriate. Additional challenges include the wide range of educational attainment as well as gender effects on educational opportunities. A bilingual neuropsychologist who is trained and licensed in the U.S. and a bilingual psychometrics expert are deeply involved in harmonization processes. Conclusion: Harmonized KBASE cognitive data will be shared with the ADSP and other investigators for use in quantitative phenotype analyses, as well as other pooled, integrative, and comparative studies of other multiethnic, multilingual, U.S., and international cohorts. [ABSTRACT FROM AUTHOR]

Individuals with more complex jobs experience better cognitive function in old age and a lower risk of dementia, yet complexity has multiple dimensions. Drawing on the Social Networks in Alzheimer Disease study, we examine the association between occupational complexity and cognition in a sample of older adults (N = 355). A standard deviation (SD) increase in complex work with people is associated with a 9% to 12% reduction in the probability of mild cognitive impairment or dementia, a 0.14–0.19 SD increase in episodic memory, and a 0.18–0.25 SD increase in brain reserve, defined as the gap (residual) between global cognitive function and magnetic resonance imaging (MRI) indicators of brain atrophy. In contrast, complexity with data or things is rarely associated with cognitive outcomes. We discuss the clinical and methodological implications of these findings, including the need to complement data‐centered activities (e.g., Sudoku puzzles) with person‐centered interventions that increase social complexity. [ABSTRACT FROM AUTHOR]

Objective: Neuropsychiatric symptoms (NPS) are common in mild cognitive impairment (MCI) and even more in Alzheimer's disease (AD). The symptom‐based cluster including nighttime disturbances, depression, appetite changes, anxiety, and apathy (affective and vegetative symptoms) was associated with an increased risk of dementia in MCI and has common neuroanatomical associations. Our objective was to investigate the differences in brain morphology associations with affective and vegetative symptoms between three groups: cognitively normal older adults (CN), MCI and AD. Material and Methods: Alzheimer's Disease Neuroimaging Initiative data of 223 CN, 367 MCI and 175 AD, including cortical volumes, surface areas and thicknesses and severity scores of the five NPS were analyzed. A whole‐brain vertex‐wise general linear model was performed to test for intergroup differences (CN‐MCI, CN‐AD, AD‐MCI) in brain morphology associations with five NPS. Multiple regressions were conducted to investigate cortical change as a function of NPS severity in the AD‐MCI contrast. Results: We found (1) signature differences in nighttime disturbances associations with prefrontal regions in AD‐MCI, (2) signature differences in NPS associations with temporal regions in AD‐MCI for depression and in CN‐AD for anxiety, (3) decreased temporal metrics in MCI as nighttime disturbances and depression severity increased, (4) decreased pars triangularis metrics in AD as nighttime disturbances and apathy severity increased. Conclusion: Each NPS seems to have a signature on brain morphology. Affective and vegetative NPS were primarily associated with prefrontal and temporal regions. These signatures open the possibility of potential future assessments of links between brain morphology and NPS on an individual level. Key points: Signature differences in nighttime disturbances associations with prefrontal regions in Alzheimer's disease mild cognitive impairment (AD‐MCI).Signature differences in Neuropsychiatric symptoms associations with temporal regions in AD‐MCI for de‐pression and in cognitively normal‐AD for anxiety.Dreased temporal metrics in MCI as nighttime dis‐turbances and depression severity increased.Decreased pars triangularis metrics in AD as nighttime disturbances and apathy severity increased. [ABSTRACT FROM AUTHOR]

Background: Abnormal tau protein aggregation and spreading in the brain is closely linked to cognitive impairment and neurodegeneration in Alzheimer's disease (AD). The role of genetic variation in brain tau aggregation has not been fully elucidated. Here, we performed pathway‐based candidate gene association analysis to identify novel genes associated with brain tau deposition on PET in a well‐characterized Korean AD continuum Cohort. Method: We analyzed data for 145 older adults (69 cognitively normal (CN), 38 mild cognitive impairment (MCI), and 38 AD) from the Korean Brain Aging Study for the Early Diagnosis and Prediction of AD (KBASE). We selected 15 candidate genes related to both tau pathways and AD, curated from existing pathway databases, literature mining, and genome‐wide association study findings. We performed gene‐based association analysis of tau deposition measured by 18F‐AV‐1451 PET scans using PLINK. In addition, we performed whole brain voxel‐wise association analysis of identified single nucleotide polymorphisms (SNPs) to investigate their topographic distributions of differences in tau deposition across the whole brain using SPM12. We examined the interaction of the identified SNPs with APOE ε4 carrier status. Result: Gene‐based analysis identified CLU and FYN as significantly associated with tau deposition after adjusting for multiple testing. The most significant signals originated from rs149413552 in CLU and rs57650567 in FYN. A voxel‐wise analysis for the two SNPs showed that individuals carrying at least one minor allele demonstrated greater tau throughout the temporal, parietal, and frontal lobes than individuals having no minor alleles (Fig. 1). The two SNPs were associated with greater cortical atrophy of AD‐signature regions and worse global cognition. rs149413552 in CLU was also associated with longitudinal decline in global cognition and immediate verbal memory over two years. APOE ε4 and the two SNPs appeared to exert additive effects on tau levels (Fig. 2). An interaction between rs57650567 in FYN and APOE ε4 carrier status was observed (Fig. 2) Conclusion: CLU and FYN may play a specific role in tau pathophysiology. These risk genes may prove useful for biomarker and therapeutic development. Larger and multiethnic studies are needed to confirm these findings and to determine generalizability across populations. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative disorder that progresses over time, but its exact cause remains unknown. While risk genomic loci for AD‐related biomarkers have been identified in previous studies, these findings have mostly been based on European cohorts. As a result, there is limited research on the African American (AA) population. To address this research gap, we conducted a Genome‐Wide Association Study (GWAS) using whole genome sequencing (WGS) data from Alzheimer's Disease Sequencing Project (ADSP) to identify AD‐related variants in the AA population. Method: WGS data and harmonized phenotypes from the cognitive, fluid biomarker, and neuropathology domains were downloaded from the ADSP database. Quality control (QC) was performed on the WGS data through removing high missing call rate SNPs, rare genetic variants, genetically closely related subjects, subjects with high missing call rate, and genomic loci that failed the Hardy‐Weinberg equilibrium among cognitive normal cohort. After QC 9,815,631 SNPs and 33,324 subjects were preserved. A total of 5,397 subjects in AA population were selected for the subsequent GWAS analysis. Scalable and Accurate Implementation of Generalized mixed model (SAIGE) was used to perform association tests between genetic variants and 39 phenotypic traits controlled for age, sex, and the first 10 principal components. Functional mapping and annotation (FUMA) was used to perform the post GWAS annotations. Result: Thirty‐nine phenotypic traits from cognitive, fluid biomarker, and neuropathology domains were screened but only the "infarcts and lacunes", "PD braak (dichotomous)", and "whole brain vascular disease" of the neuropathology data had significant GWAS signals. We identified one risk locus for each of the phenotypic traits: lead SNP rs7604487 significantly associated with "infarcts and lacunes"; lead SNP rs7093080 significantly associated with "PD braak (dichotomous)"; and lead SNP rs623123 significantly associated with "whole brain vascular disease" (Figure 1). These associations were insignificant in the ADSP cohort with European ancestry. Conclusion: Our GWAS on AD related biomarkers identified three genetic loci associated with multiple neuropathological processes among the AA population. These findings have a potential to contribute to a better understanding of the AD genetic risk in the AA population. It warrants further investigation to replicate the discovery in independent cohorts. [ABSTRACT FROM AUTHOR]

Background: Aging is a key risk factor for Alzheimer's disease (AD) and has a significant structural impact on the brain. Machine learning approaches have been developed to estimate the biological age of the brain from MRI scans. Here, we performed genome‐wide association (GWA) analysis of accelerated brain aging in two independent Korean AD cohorts. Method: Participants included 1,738 Korean older adults from two independent cohorts that consisted of preclinical and clinical stages of AD (n = 1,209 from the K‐ROAD cohort (Korea‐Registries to Overcome and Accelerate Dementia Research Project) and n = 529 from the KBASE cohort (Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease)). Brain age was estimated from structural MRI scans using brainageR. Brain age acceleration (predicted brain age ‐ chronological age) was calculated for each participant. Genetic variants associated with brain age acceleration were assessed after accounting for age, sex, APOE e4 carrier status, and diagnosis. Meta‐analysis was performed using METAL, followed by transcriptomic analysis. Polygenic risk scores for brain age acceleration were calculated with PRS‐CS using GWAS meta‐analysis summary statistics, followed by molecular imaging analysis. Result: GWA meta‐analysis identified six intronic SNPs in the LRBA (Lipopolysaccharide responsive Beige‐like Anchor) locus on chromosome 4 as significantly associated with brain age acceleration with the most significant signal at rs7699001 (Fig. 1; p<5´10−8). LRBA is highly expressed in the brain, especially in microglia. The LRBA gene is differentially expressed in AD compared to cognitively normal older adults. rs7699001 was associated with expression levels of the LRBA gene in tissue from several organs including brain. rs7690001 has been reported to be associated with inferior parietal cortical thickness in non‐Hispanic whites (p = 5´10−3). Polygenic risk scores of brain age acceleration in the K‐ROAD cohort were significantly associated with amyloid‐β deposition measured from amyloid PET scans (Fig. 2; p = 2.7´10−4). Conclusion: LRBA is a novel genetic risk factor for brain aging in the Korean population with relevance for AD. The microglial LRBA protein may be involved in phagocytosis, which has been implicated in aging processes and AD progression. Replication in other populations and mechanistic follow‐up studies are warranted. This work was supported by U01AG072177. [ABSTRACT FROM AUTHOR]

Background: We harmonized composite scores for memory, executive functioning (EF), and language from granular cognitive data from the National Alzheimer's Coordinating Center (NACC) uniform dataset on 39,965 individuals. We explored if our cognitive scores correlated with APOE genotype and structural magnetic resonance imaging data. Method: In our previously published harmonization methods we used confirmatory factor analysis models, guided by theoretical considerations from content experts, to estimate domain scores. For this cross‐sectional analysis of last visits in participants over age 60, we obtained APOE genotype data (n = 28,558 individuals) and FreeSurfer regional volume data (n = 2,404 individuals). We looked at relationship between cognitive performance and APOE genotype grouping by disease group (normal cognition, mild cognitive impairment (MCI), or AD dementia). We ran student's two sample t‐test to assess the effect of APOE ε4 genotype on cognitive domain score. We also ran regression models for cognitive domain scores on each FreeSurfer region selected a priori on the basis of assumed association on that index domain adjusting for age, gender and APOE genotype. Result: Relevant demographic and clinical data are summarized in Table 1. Associations with APOE genotype and each cognitive domain are shown in Figure 1. As expected, participants with at least one APOE ε4 alleles compared with none had lower composite memory scores (p<0.01, t‐test) compared with those with none in participants with dementia and MCI. APOE ε4 carriers also had lower language scores in participants with dementia (p = 0.037, t‐test). Among the FreeSurfer regions, composite memory scores were strongly associated with hippocampal volume, parahippocampus and entorhinal cortex thickness across all dementia categories (Table 2). EF scores were associated with 2 EF brain regions of interest in participants with dementia. Language scores were associated with 2 language regions of interest in participants with dementia. Conclusion: Our analyses shows composite scores are associated with APOE genotype in participants with dementia and relevant brain regions in the memory domain across all dementia categories. Our analyses shows that our cognitive scores correlate with memory brain regions and APOE genotype in expected directions. [ABSTRACT FROM AUTHOR]

Background: Amyloid‐β and tau proteins accumulate at distinct neuronal systems in Alzheimer's disease (AD). However, why specific brain regions are more vulnerable to amyloid‐β and tau pathologies than others remain unclear. Method: Using two independent cohorts from the ADNI and the IADRC as discovery and replication samples, we studied the relationship between gene expression profiles and amyloid‐β and tau pathologies. In ADNI, we included 583 and 564 participants with amyloid‐β PET and tau PET data, respectively; in IADRC, we included 110 and 78 participants with amyloid‐β PET and tau PET data, respectively. In both cohorts, amyloid‐β pathology was measured by [18F]florbetapir (AV45) and [18F]florbetaben (FBB) PET; tau pathology was measured by [18F]flortaucipir (AV‐1451) PET. The regional vulnerability to amyloid‐β and tau pathologies were estimated by contrasting amyloid‐β and tau PET data between cognitively unimpaired (CU and/or SCD) and patients (MCI and AD). Regional expression profiles for 20,736 protein‐coding genes were derived from brain‐wide microarray‐based transcriptome data from the Allen Human Brain Atlas. We computed spatial associations between gene expression levels and amyloid‐β (gene‐to‐amyloid‐β associations) and tau (gene‐to‐tau associations) pathologies for 60 AD susceptibility genes selected from recent large‐scale GWAS studies. Result: In both cohorts, AD and MCI showed the expected higher amyloid‐β loads than CU, particularly in the medial parietal cortex, temporal lobe, medial and inferior prefrontal cortices, and superior and middle frontal cortices. In both cohorts, AD and MCI showed higher tau deposits than CU, mainly in the medial temporal lobe, medial and inferior parietal cortices, and inferior and middle temporal cortices. In both cohorts, we identified consistent significant gene‐to‐amyloid‐β and gene‐to‐tau associations after multiple testing adjustment (PFDR < 0.05). Specifically, CNTNAP2 and CLU showed the strongest positive gene‐to‐amyloid‐β associations, whereas TNIP1 and INPP5D showed the strongest negative associations. APOE and BIN1 showed the strongest positive gene‐to‐tau associations, whereas SORL1 showed the strongest negative associations. Conclusion: We identified replicable regional vulnerability patterns of amyloid‐β and tau pathologies and their relationships to regional gene expression profiles. Better understanding of the regional association of gene expression and amyloid‐β and tau pathologies may foster identification of novel diagnostic and therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: The human brain undergoes considerable morphologic change with age, which is one of the primary risk‐factors for Alzheimer's disease (AD). Age‐related microstructural changes in the brain may be quantified by advanced diffusion MRI (dMRI) with compartment modeling. In this longitudinal study, we aimed to investigate the effects of age on the hippocampal subfields, one of the brain regions vulnerable to the early stages of AD. Method: Thirty‐seven cognitively normal older adults (70.6 ± 6.7 years) were recruited from the Indiana ADRC. Participants received a baseline and a follow‐up MRI scan over 24 ± 11.7 months with a Siemens 3T Prisma scanner using a 64‐channel head coil. We applied grey‐matter‐specific multi‐compartment diffusion model (cortical‐neurite orientation dispersion and density imaging) to derive diffusion microstructural metrics, namely orientation dispersion index (ODI), volume fractions of fast isotropic diffusion (VFISO), and intracellular‐space (VFIC) in the hippocampal‐subfields (CA1‐3, CA4 and dentate gyrus (CA4‐DG), and subiculum) [1]. To study the longitudinal age effects, we investigated the rate of change in the diffusion metrics and the association between the rate of change and age. Linear regression analysis was performed using SPSS software and adjusted for sex, education, APoE e4 status, and subfield volumes (Table 1). Result: All subjects had normal cognition (i.e., MoCA and MMSE, Table 1). The volumes of CA1‐3 and subiculum significantly decreased between the baseline and follow‐up MRI scans, (p<0.05, Table 1). During this period, while significantly increased ODI within subject was observed in CA4‐DG, significantly decreased VFISO in all the subfields and increased VFIC in the subiculum were noted (Figure 1). The decreasing rate of VFISO in CA4‐DG was significantly associated with age (Table 2), suggesting the reduction in VFISO accelerated over age. Conclusion: We demonstrated that diffusion metrics are sensitive to microstructural alterations in the hippocampal subfields of cognitively normal older adults. The cornu ammonis region 4 and dentate gyrus is the most vulnerable region affected by aging with increased tissue dispersion and age‐accelerated reduction in fast isotropic diffusion. Future studies will focus on the effects of the initial microstructural condition to its rate of change with aging. References: 1. Shahid, et al., Brain,2022 [ABSTRACT FROM AUTHOR]

Background: Although several prior diffusion MRI studies have indicated a role of medial temporal lobe (MTL) white matter microstructural decline in Alzheimer's disease (AD), it is unclear which AD risk genes are associated with this decline. The goal of this study is to leverage multi‐site harmonized diffusion MRI data to investigate the relationship between 73 AD risk variants and microstructural decline in MTL tracts. Method: The dataset used in this study was collated from three longitudinal cohorts of aging [Alzheimer's Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)]. In total, this dataset included 1,308 participants (70% cognitively unimpaired, 27% MCI, 3% AD) aged 50+ who had both diffusion MRI and genotype data and includes a total of 3,442 imaging sessions (mean number of visits: 2.68 ± 1.66, interval range: 1 ‐ 12 years). Genotype data was preprocessed using a standardized approach, and the dosage of the 73 AD risk variants was extracted. Diffusion MRI data was preprocessed using the PreQual pipeline and free‐water (FW) correction was conducted to obtain FW and FW‐corrected fractional anisotropy (FAFWcorr) maps. FW and FAFWcorr were quantified within 5 MTL tracts, including the cingulum, fornix, uncinate fasciculus, inferior longitudinal fasciculus, which were subsequently harmonized using the Longitudinal ComBat package. Linear regression was conducted to investigate the association between AD risk variants and MTL microstructure at baseline, while linear mixed effects regression was used for longitudinal analysis. All models were corrected for age at baseline and sex and corrected for multiple comparisons using the FDR approach. Result: At baseline, APOE‐ε4 dosage was strongly associated with MTL FW in all tracts, with additional associations for TMEM106B and CR1 dosages with fornix FW (Table 1). Longitudinally, APOE‐ε4 dosage was a strong predictor of MTL FW decline in all tracts, and KAT8, CR1, and TMEM106B dosages exhibited significant associations with fornix microstructural decline (Table 2 and Figure 1). Conclusion: This study suggests that although APOE‐ε4 is the strongest predictor of white matter microstructural decline, several other AD risk genes (i.e., CR1, SPI1, TMEM106B, KAT8) contribute to decline, particularly in the fornix. [ABSTRACT FROM AUTHOR]

Background: There is evidence that Alzheimer's Disease (AD) may have distinct subtypes. Most approaches for identifying subgroups use cross‐sectional data. Here we use longitudinal cognitive test data from two large cohort studies to determine if there are distinct patterns of cognitive decline among those with AD and if those patterns differ across three domains of cognition. Method: We used data from the Rush Memory and Aging Project and Religious Orders Study (Table 1). Previously, cognitive test items were assigned to a single domain of memory, language, or executive function and scores for each domain were estimated using confirmatory factor analysis. We used Growth Mixture Models to estimate trajectories of decline starting at AD diagnosis and identified subgroups with differing trajectories. Each domain was modeled individually and in a combined model that included distinct latent processes for each domain. Models with one to five classes were estimated and the number of classes was selected using Bayesian Information Criterion (BIC) and entropy. Result: The two class models fit best for each of the individual domains (Figure 1). For memory and language one class was characterized by a higher intercept and steeper slope while the other had a lower intercept and flatter slope. For executive function the two classes had similar intercepts, but different slopes. Five classes best fit the data in the combined model. The pattern of decline within a class was similar across the three domains (Figure 2). For each domain, four of the classes were differentiated from each other by lower intercepts and increasingly steep slopes, while a fifth class had the lowest intercept, but a flatter trajectory over time. Conclusion: We did not see different patterns across domains of cognition; knowing the trajectory of one cognitive domain explains the most likely trajectory of the other domains. Still, differences identified in patterns of overall decline may be important for AD treatment. Future work will focus on determining biological relevance of subgroups through association with genetic and other biomarkers as well as addressing methodological issues such as incorporating survival time and accounting for the presence of prevalent cases and floor scores. [ABSTRACT FROM AUTHOR]

Background: Transcriptional risk scores (TRS), a summation of polarized expression levels of functional genes, have been proposed for disease risk assessment using transcriptome profiles. Our previously developed blood‐based TRS showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain‐based TRS models and calculated TRS using brain tissue‐based transcriptome profiles. Method: We integrated AD genome‐wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization and then calculated individual TRS by summing the polarized z‐scores over the target genes. We used brain transcriptome data from the Religious Orders Study and Rush Memory and Aging Project cohort (ROSMAP; N = 634) as a discovery set and the Mount Sinai Brain Bank cohort (MSBB; N = 244) as a replication set. We performed association analysis of TRS with AD diagnosis, amyloidopathy, tauopathy, and cross‐sectional and longitudinal cognitive function. We compared AD classification performance of TRS with polygenic risk scores (PRS) using a machine learning approach (Fig. 1). Result: Higher TRS values were significantly associated with AD compared to cognitively normal controls (Table 1). Higher TRS values were also related with amyloidopathy, tauopathy, poor cognitive performance, and faster cognitive decline. The AD classification performance of PRS was increased with the inclusion of TRS up to 12% with the area under the curve value of 0.842 (Fig. 2). These findings were replicated in the replication dataset. Conclusion: Our results showed that brain‐based TRS is associated with AD pathology and clinical course. Furthermore, TRS improves the AD classification performance of PRS. Along with static PRS, dynamic TRS based on transcriptional expression levels at a given time could be a potential disease‐stage specific AD biomarker. [ABSTRACT FROM AUTHOR]

Background: The study examined whether trust in researchers affects interest in AD (Alzheimer's disease) biomarker research participation, specifically AD blood biomarkers and MRI and PET neuroimaging, and whether this was independent of other factors commonly associated with hesitancy to participate in research, including race and knowledge of AD. Methods: 258 community‐dwelling Black and 141 non‐Hispanic White adults (age ≥ 55) who had never participated in AD research completed a survey about their perceptions of AD biomarker research. The survey asked participants to select one of three responses, "Yes," "Unsure," and "No" to 6 trust statements, with yes being the highest level of trust. Questions specifically assessed whether survey participants trusted researchers to keep information confidential, be honest, component, not cause harm, treat them equally, and share results with them and their community. Those who answered yes to all 6 questions were categorized as having very high trust, and those who answered no or unsure to any of the 6 questions were categorized as having lower trust. Ordinal logistic regression models were used for the analysis of outcome variables with three possible responses (i.e. interest in participation in an AD biomarker study and individual procedures, motivators for research participation). We adjusted for age, education, sex, race, and knowledge about AD. Result: Participants with very high trust in researchers were less hesitant about doing AD biomarker research (OR 0.32 (95% CI (0.20‐0.52), P < 0.001). Participants with concerns about confidentiality, study risks, and being treated equally had greater hesitancy about doing AD biomarker research (P's < 0.05). Concerns about researchers' competency were associated specifically with lower interest in doing neuroimaging procedures (P's < 0.05). Very high trust in researchers was associated with greater interest in return of results, particularly brain scans (OR 1.92 (95% CI 1.06‐3.48), P = 0.032). Conclusion: Even after other factors known to affect AD research participation, trust remains a highly significant factor affecting the interest in AD research participation in blood biomarker neuroimaging procedures. Recruitment strategies for AD biomarker research may need to address participants' trust concerns, specifically confidentiality, study risks, and equity. [ABSTRACT FROM AUTHOR]

Introduction: Limbic age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC) is common in advanced age and can underlie a clinical presentation mimicking Alzheimer's disease (AD). We studied whether an autopsy‐derived fluorodeoxyglucose positron emission tomography (FDG‐PET) signature of LATE‐NC provides clinical utility for differential diagnosis of amnestic dementia patients. Methods: Ante mortem FDG‐PET patterns from autopsy‐confirmed LATE‐NC (N = 7) and AD (N = 23) patients were used to stratify an independent cohort of clinically diagnosed AD dementia patients (N = 242) based on individual FDG‐PET profiles. Results: Autopsy‐confirmed LATE‐NC and AD groups showed markedly distinct temporo‐limbic and temporo‐parietal FDG‐PET patterns, respectively. Clinically diagnosed AD dementia patients showing a LATE‐NC–like FDG‐PET pattern (N = 25, 10%) were significantly older, showed less abnormal AD biomarker levels, lower APOE ε4, and higher TMEM106B risk allele load. Clinically, they exhibited a more memory‐predominant profile and a generally slower disease course. Discussion: An autopsy‐derived temporo‐limbic FDG‐PET signature identifies older amnestic patients whose clinical, genetic, and molecular biomarker features are consistent with underlying LATE‐NC. [ABSTRACT FROM AUTHOR]

Introduction: The study examined Black and White prospective participants' views of barriers to and facilitators of participation in Alzheimer's disease (AD) biomarker research. Methods: In a mixed‐methods study, 399 community‐dwelling Black and White older adults (age ≥55) who had never participated in AD research completed a survey about their perceptions of AD biomarker research. Individuals from lower socioeconomic and education backgrounds and Black men were over‐sampled to address perspectives of traditionally under‐represented groups. A subset of participants (n = 29) completed qualitative interviews. Results: Most participants expressed interest in biomarker research (overall 69%). However, Black participants were comparatively more hesitant than White participants (28.9% vs 15.1%), were more concerned about study risks (28.9% vs 15.1%), and perceived multiple barriers to participating in brain scans. These results persisted even after adjusting for trust and perceived knowledge of AD. Information was a primary barrier (when absent) and incentive (when provided) for AD biomarker research participation. Black older adults desired more information about AD (eg, risk, prevention), general research processes, and specific biomarker procedures. They also desired return of results to make informed decisions about their health, research‐sponsored community awareness events, and for researchers to mitigate the burden placed on participants in research (eg, transportation, basic needs). Conclusion: Our findings increase representativeness in the literature by focusing on individuals with no history of AD research experience and those from traditionally underrepresented groups in research. Results suggest that the research community needs to improve information sharing and raising awareness, increase their presence in the communities of underrepresented groups, reduce incidental costs, and provide valuable personal health information to participants to increase interest. Specific recommendations for improving recruitment are addressed. Future studies will assess the implementation of evidence‐based, socioculturally sensitive recruitment strategies to increase enrollment of Black older adults into AD biomarker studies. HIGHLIGHTS: Individuals from under‐represented groups are interested in Alzheimer's disease (AD) biomarker research.After adjusting for trust and AD knowledge, Black participants were still more hesitant.Information is a barrier (when absent) to and incentive (when given) for biomarker studies.Reducing burden (e.g., transportation) is essential for recruiting Black older adults. [ABSTRACT FROM AUTHOR]

Introduction: White matter microstructure may be abnormal along the Alzheimer's disease (AD) continuum. Methods: Diffusion magnetic resonance imaging (dMRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 627), Baltimore Longitudinal Study of Aging (BLSA, n = 684), and Vanderbilt Memory & Aging Project (VMAP, n = 296) cohorts were free‐water (FW) corrected and conventional, and FW‐corrected microstructural metrics were quantified within 48 white matter tracts. Microstructural values were subsequently harmonized using the Longitudinal ComBat technique and inputted as independent variables to predict diagnosis (cognitively unimpaired [CU], mild cognitive impairment [MCI], AD). Models were adjusted for age, sex, race/ethnicity, education, apolipoprotein E (APOE) ε4 carrier status, and APOE ε2 carrier status. Results: Conventional dMRI metrics were associated globally with diagnostic status; following FW correction, the FW metric itself exhibited global associations with diagnostic status, but intracellular metric associations were diminished. Discussion: White matter microstructure is altered along the AD continuum. FW correction may provide further understanding of the white matter neurodegenerative process in AD. Highlights: Longitudinal ComBat successfully harmonized large‐scale diffusion magnetic resonance imaging (dMRI) metrics.Conventional dMRI metrics were globally sensitive to diagnostic status.Free‐water (FW) correction mitigated intracellular associations with diagnostic status.The FW metric itself was globally sensitive to diagnostic status. Multivariate conventional and FW‐corrected models may provide complementary information. [ABSTRACT FROM AUTHOR]