Citations

AbstractBackgroundAlzheimer’s disease (AD) patients have decline in cognitive domains including memory, language, visuospatial, and/or executive function and brain pathology including amyloid‐β and tau deposition, neurodegeneration, and frequent vascular co‐pathologies detectable by neuroimaging and/or cerebrospinal fluid biomarkers. However, molecular disease mechanisms are complex and heterogeneous. It is necessary to develop cost‐effective blood‐based biomarkers reflecting brain molecular perturbations in AD. We identified blood‐based gene and co‐expression network level changes associated with AD/mild cognitive impairment (MCI) diagnosis and AD‐related phenotypes.MethodWe performed differential gene expression and weighted gene co‐expression network analysis, followed by meta‐analysis, using blood transcriptome data of 391 participants from the Mayo Clinic Study of Aging and 654 participants from the Alzheimer's Disease Neuroimaging Initiative. The neuroimaging phenotypes include microhemorrhages, infarcts, amyloid burden, hippocampal volume, and white matter hyperintensities. The cognitive phenotypes include standardized cognitive subtest scores and composite scores for memory, language, visuospatial, and executive function.ResultFive out of 18 modules(M) are significantly associated with diagnosis or cognition (FDR‐adjusted p<0.05). M1 and M15 both positively associates with memory, M1 positively associated with language and M15 with visuospatial function. M1 and M15 are enriched in differentially expressed genes (DEGs) associated with language and executive function, respectively. M2 negatively associates with logical memory delayed recall scores(LMDR), memory, executive, and language functions and is enriched in DEGs for these phenotypes. M8 negatively associates with memory, language and executive functions and is enriched in DEGs for memory and language. M12 positively associates with LMDR. M1 and M15 are down‐regulated while M2 and M8 are up‐regulated in AD/MCI patients. Cell‐type enrichment analysis showed M2 is enriched in monocytes and neutrophils; M8 in monocytes; M15 in B cells (FDR <0.05). Gene ontology terms enriched in these modules indicated broad consistency with their cell types.ConclusionWe identified five modules significantly associated with AD/MCI or cognitive phenotypes using blood transcriptome data. These findings nominate blood transcriptome changes and their enriched biological processes as potential pathomechanisms in cognitive decline and AD/MCI development. We aim to investigate these blood transcripts as potential biomarkers for AD or AD‐related phenotypes and therapeutic targets through additional replication and experimental validation studies.

AbstractBackgroundOlder adults with dual cognitive and mobility impairments have higher financial costs and poorer quality of life than adults with either impairment alone. Blood biomarkers of Alzheimer Disease (AD) pathology (Aβ42, Aβ40 and p‐tau181) and neurodegeneration (neurofilament light (NFL) and glial fibrillary acidic protein (GFAP)) may identify individuals at risk for both mobility and cognitive impairment and provide novel insights into mechanistic underpinnings.MethodBlood biomarkers (SiMoA Quanterix N4PE, p‐tau181 single‐plex) were available in a subsample of 1751 ARIC study participants at Visit 5 (V5, 2011‐13, mean age 76.2 years (5.3), 41% men, 28% Black); Aβ42 was reversed and adjusted for Aβ40 (‐Aβ42|40) for consistent directions of associations (higher=worse). Usual gait speed and cognitive status were assessed over 8 years at V5, V6 (2016‐17), and V7 (2017‐18). Participants were categorized by V5 impairment: mobility‐only (gait speed <0.8m/s, n=430), cognitive‐only (expert‐adjudicated dementia, n=38), dual (n=54), or neither (referent, n=1229). Multinomial regression models examined relative risk ratios (RRR) of V5 log‐transformed biomarkers with prevalent impairments and, among those with neither impairment at V5, incident impairments through V7. All models adjusted for V5 age, sex, self‐reported race (Black or White), renal function, and body mass index.ResultHigher levels of all biomarkers were associated with greater risk of prevalent impairment in mobility, cognitive, or dual domains (Figure A). Over 8‐years of follow‐up, incident impairments included 471 participants with only mobility impairments, 39 with only cognitive impairments, 55 with dual impairments, and 168 died without developing any impairment. All biomarkers were associated with incident dual impairment per IQR higher biomarker: p‐tau181 (RRR=2.54; 95% CI: 1.45, 4.44), ‐Aβ42|40 (RRR=1.66; 1.31, 2.09), NfL (RRR=2.69; 1.22, 5.91) and GFAP (RRR=2.13; 1.07, 4.25). P‐tau181, ‐Aβ42|40, and GFAP were also associated with incident cognitive‐only impairment, while P‐tau181, NFL and GFAP were associated with incident mobility‐only impairment. (Figure B).ConclusionBlood biomarkers of brain health may be useful tools for earlier identification of those at risk for cognitive, mobility, or dual declines, facilitating earlier targeted interventions. Potential differences in AD‐pathology versus neurodegeneration blood biomarker relationships with outcomes of mobility and cognition may help to identify more targeted interventions for both.

AbstractBackgroundSelective corticolimbic vulnerability to tau pathology in Alzheimer’s disease (AD) underlies clinicopathologic heterogeneity. The goal of this presentation will be to examine spatial heterogeneity of tangle distribution on a continuum through the utility of the corticolimbic index (CLix).MethodWe will discuss the development of CLix in the Florida Autopsied Multi‐Ethnic (FLAME) cohort, which sought to collapse the spatial distribution of thioflavin‐S tangle counts in AD (n=1361) to assign a continuum: hippocampal sparing with cortical predominance (<10), representative/typical (≥10 to <30), and limbic predominant with cortical sparing (≥30). The relationship with atypical, non‐amnestic clinical presentations will be described. Relevant clinical measures, including age at symptomatic onset and longitudinal decline on mini‐mental state examination (MMSE) will be examined. Digital pathology measures of AD (tau [AT8, GT‐38], amyloid‐β [6F/3D]) and glial activation (astrogliosis: GFAP, activated microglia/macrophage: CD68) will be shown in hippocampus and association cortices (n=60). To further extend these findings to the antemortem space, CLix score will be examined in an independent, autopsied neuroimaging group (n=93).ResultsLower CLix score correlated with younger age at symptomatic onset (R=0.39, p<0.001), faster MMSE decline (R=0.27, p<0.001), and associated with non‐amnestic clinical presentations (median=13 vs. 21, p<0.001). Lower CLix score correlated with greater hippocampal volume on MRI (R=‐0.45, p<0.001) and higher flortaucipir‐PET uptake (e.g., posterior cingulate‐precuneus cortex: R=‐0.74, p<0.001). Advanced tangle maturity burden (GT‐38) differed across all regions (p<0.001). Hippocampal sparing AD exhibited higher cortical tau pathology and reduced cortical activated microglia/macrophage.ConclusionsCorticolimbic tangle distribution links with AD clinical features and neuroimaging changes. Reduced activated microglia/macrophage in association cortices of relative hippocampal sparing AD may contribute to higher tau burden. Corticolimbic vulnerability should be considered for personalized therapies targeting immune dysregulation.

AbstractBackgroundSleep’s crucial role in maintaining brain health is increasingly recognized, particularly due to the rising prevalence of neurodegenerative diseases. It not only supports cognitive function but also aids in clearing brain metabolic waste through cerebrospinal fluid (CSF) dynamics. During sleep, especially in the non‐rapid eye movement (NREM) phase, CSF flow increases, essential for removing neurotoxic substances like amyloid‐beta proteins. This study explores the link between sleep respiratory patterns and CSF flow promotion.MethodElectroencephalography (EEG), and functional magnetic resonance imaging (fMRI) were simultaneously acquired during sleep from Gu, et al. (n = 29) (Gu et al. 2022). Respiration, measured via a respiratory belt, calculated respiratory volume (RV) as the standard deviation within a 5 second sliding window. EEG data processed delta band power (1‐4Hz) averaged across channels. Parcellated fMRI data using the brain template followed by the calculation of the averaged gray matter (GM) blood‐oxygen‐level‐dependent (BOLD) signal using the GM mask. CSF inflow was manually extracted from the fMRI’s bottom slice at the foramen magnum level. Subsequently, all signals were temporally aligned to the peak of the CSF inflow signal.ResultAt the time of CSF inflow (time = 0 sec. in the Figure 1A‐D), we observed an earlier increase in RV and EEG delta power, followed by changes in the GM BOLD signal (Figure 1). EEG and RV changes preceded the BOLD signal changes, which, in turn, preceded changes in CSF flow. Sensitivity to detect RV involvement during MRI varied with the placement of the respiratory belt, being least sensitive on the chest and more on the abdomen.ConclusionThese findings raise an interesting question on the role respiratory patterns during sleep, particularly in the NREM phase. Speculatively, RV change may influence CSF circulation during NREM sleep (Figure 1E). This enhancement in CSF flow is crucial for the clearance of metabolic waste from the brain, potentially reducing the risk of neurodegenerative diseases. Our findings indicate that respiratory volume may serve as a significant marker for CSF circulation during sleep.

AbstractBackgroundThe prevalence of dementia in populations that are underrepresented in research is projected to increase with population aging. Latino and Black individuals, the largest underrepresented populations in the US, experience a higher dementia burden than non‐Hispanic White individuals, which is not explained by ethnoracial biological factors. Disparities in structural and social determinants of health may contribute to the preponderance of medical conditions known to increase dementia risk, including potentially modifiable factors in Latino and Black communities. To assess the scope of this problem, we measured the frequency of common modifiable dementia risk factors in community‐dwelling, cognitively normal, Latino and Black adults.MethodCognitively normal community‐dwelling Latino and Black adults, ≥45‐years‐old, were enrolled within our longitudinal study of memory and aging at Mayo Clinic in Florida (Jacksonville, FL) and completed a baseline assessment between 3/2022 and 10/2023. Demographics, and self‐reported race/ethnicity and history of dementia risk factors were recorded. Dementia risk factors were objectively sampled through clinical interview (depression: Geriatric Depression Scale ≥5), examination (hypertension: BP >130/80 mmHg; obesity: BMI≥30), and serum analyses (hyperlipidemia: total cholesterol<200mg/dL, HDL>50mg/dL, LDL<100mg/dL, Triglycerides<150mg/dL; diabetes: HbA1C >5.6%, vitamin B12 deficiency: <350 ng/dL).Result86 participants (43 Latino, 43 Black), 65.8 (8.8) years‐old, female (62.8%), with 16 (2.4) years of education were assessed. Dementia risk factors were frequent in Latino and Black participants, including hypertension (63%, 90%; p = 0.005), hyperlipidemia (93%, 89%; p = 0.702), obesity (33%, 44%; p = 0.370), diabetes (60%, 47%; p = 0.370), vitamin B12 deficiency (21%, 21%; p = 0.989), and depression (5%, 24%; p = 0.781). These risk factors were unrecognized or suboptimally managed in a substantial proportion of Latino and Black participants: hypertension (56%, 73%; p = 0.170), hyperlipidemia (80%, 73%; p = 0.591), diabetes (57%, 47%; p = 0.502), vitamin B12 deficiency (21%, 21%; p = 0.789), and depression (5%, 3%; p>0.99).ConclusionPotentially modifiable dementia risk factors were detected in the majority of community‐dwelling Latino and Black cognitively normal older‐adults and were frequently unrecognized or suboptimally managed. Proactive screening for known dementia risk factors may provide opportunities for targeted interventions to improve brain health and decrease dementia disparities in underrepresented populations.

AbstractBackgroundScalable, efficient methods are needed to enroll diverse populations of older adults into AD observational studies and clinical trials. We evaluated preliminary feasibility of a novel, digital, culturally informed approach to recruit and screen participants for the Alzheimer’s Disease Neuroimaging Initiative (ADNI4).MethodsDigital advertising tailored towards Black/African American and Latinx older adults residing near six clinical ADNI sites directed potential participants to a recruitment website (Figure 1). They completed digital surveys of demographics, ADNI exclusion criteria, memory concerns and changes, self‐report cognitive impairment, and the Everyday Cognition Scale (ECog)‐12 item. Participants also completed Novoic Storyteller, a self‐administered speech‐based cognitive test. Digital assessment results were used to prioritize those from minoritized ethnocultural groups, with low educational attainment, and possible Mild Cognitive Impairment (MCI) for remote blood draw at local Quest phlebotomy centers to obtain AD plasma biomarkers. Completion rates and digital assessment performance were compared between minoritized and non‐minoritized groups.ResultsOf 989 participants recruited from digital advertising efforts who provided contact information, 230 (23%) signed consent, and 183 (19%) completed at least one remote assessment (Table 1). 45% were from minoritized ethnocultural groups, 4% had ≤12 years of education, and 53% had evidence of possible MCI from digital assessments. 34 (19%) were referred to the plasma biomarker study, of which 21% completed blood draw. Compared to non‐minoritized participants, minoritized participants were less likely to report a diagnosed cognitive impairment or a family history of AD, less likely to have ECog scores indicative of MCI, and less likely to have a study partner. Minoritized participants had lower ECog and Novoic Storyteller completion rates.ConclusionRecruitment and assessment of a diverse cohort of older adults, including those with possible cognitive impairment, is feasible using culturally informed digital advertising. Improving study engagement and achieving educational diversity are key challenges. This approach is now being scaled up to facilitate recruitment into in‐clinic ADNI4, with the goal of enrolling 500 new participants: >50% from minoritized groups, 40% with MCI, and 80% amyloid positive across diagnostic groups. This approach can be adapted to facilitate recruitment and longitudinal assessment in other AD studies and trials.

AbstractBackgroundPopulation dementia prevalence is traditionally estimated using cohort studies, surveys, routinely‐collected administrative data, and registries. Hospital Electronic Health Records (EHRs) are comprised of rich structured and unstructured (text) clinical data that are underutilised for this purpose.We aimed to develop a suite of algorithms using routinely‐collected EHR data to reliably identify cases of dementia, as a key step towards incorporating such data in prevalence estimation. Towards this, we developed a novel predictive framework integrating data‐science and biostatistical methods.MethodTraining data were sourced via the National Centre for Healthy Ageing (NCHA) Data Platform, a linked, curated, EHR‐derived data warehouse. Individuals within the platform catchment aged >60 years with confirmed dementia were identified through hospital specialist dementia clinics. A comparison group of individuals aged >60years with EHR records without dementia was recruited from the community.A panel of clinical experts (Neurology, Geriatric Medicine) informed variable and concept selection and guided data cleaning efforts within both streams. Algorithms were developed via two work‐streams; a traditional biostatistical approach to fit logistic regression models using structured data elements, and a data science stream used Natural Language Processing (NLP) to fit models to the unstructured (text) parts of the EHR, for the same individuals.ResultOf 568 individuals (362 with dementia), 434 had clinical notes available. In the data science stream using unstructured data, among a range of NLP derived models, the Random Forest classifier performed best in assigning dementia status, with Area Under the Curve (AUC) 0.95, specificity 90.2% and sensitivity 88.4%. In the biostatistics stream, 15 structured variables were included in the final model, covering demographics, health service attendance, medications, and ICD‐10 Codes, with AUC 0.94, specificity 85.9% and sensitivity 85.6%.ConclusionArtificial intelligence techniques applied to unstructured electronic health data and guided by human clinical expertise may be powerful tools in capturing the presence of dementia, at least comparable to traditional techniques using structured data, and conferring practical and scientific advantages for dementia prevalence estimation. Future validation is required in less crisply delineated real‐world settings.

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder with significant environmental factors, including diet, that influence its onset and progression. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored the KD’s molecular impact for markers of AD therapeutic potential. The BEAM diet study simultaneously profiled the KD’s effect on the lipidome, blood and cerebrospinal metabolome, and microbiome of both cognitively impaired and cognitively normal individuals. The findings summarized here assess the biological impact of a Modified Mediterranean KD in the context of Alzheimer’s disease treatment and prevention.MethodBEAM involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean‐ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6‐week washout period. We employed HPLC‐MS/MS lipidomics profiling in plasma, nuclear magnetic resonance (NMR)‐based metabolomics to profile serum and CSF, and metagenomics profiling on fecal samples before and after each diet to assess dietary‐induced changes.ResultThe MMKD led to significant alterations in the blood, CSF, and microbiome. These changes included a global elevation across all plasmanyl and plasmenyl ether lipid species, improved modifiable risk factors, like increased HDL‐C and reduced BMI, the reversal of serum metabolic disturbances linked to AD such as an increase in valine levels, and a reduction in systemic inflammation. Leveraging prior clinical studies on AD (n = 1,912), we found that MMKD was inversely associated with the peripheral lipidomic signature of prevalent and incident AD. In the CSF, the MMKD was linked to modified amino acid levels and the breakdown of branched‐chain amino acids (BCAAs). Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. In addition, participants with MCI on the MMKD had lower levels of GABA‐producing microbes and GABA, and higher levels of GABA‐regulating microbes.ConclusionOur findings highlight that MMKD can improve AD‐related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood‐CSF barrier.Funding: Alzheimer’s Gut Microbiome Project, NIA U19AG063744

AbstractBackgroundChronic medical conditions increase dementia risk, and the mechanism may be captured computationally by measures of accelerated brain aging. We hypothesized that quantifying the impact of chronic conditions on GM and WM aging will aid in measuring the differential impact of each condition on brain aging and shed light on the mechanisms through which they increase dementia risk. We specifically focused on microstructural brain changes with Neurite Orientation Dispersion and Density Imaging (NODDI) based on advanced diffusion MRI ‐ neurite density index (NDI), free water fraction (FWF), and orientation dispersion index (ODI).MethodWe identified 1123 participants [913 cognitively unimpaired (CU), 114 with MCI, 96 with AD] from the Mayo Clinic Study of Aging (MCSA) and Mayo ADRC with multi‐shell diffusion MRI. MCSA participants had chronic medical conditions’ diagnosis (based on US Dept of HHS) extracted from the electronic health care records. Using the BorutaSHAP algorithm on NDI, ODI, and ISOVF features from GM and WM separately, we constructed two distinct brain age models for GM and WM. First, we evaluated the usefulness of the predicted age gap (PAG), which is the difference between predicted age by model and chronological age, for both WM and GM NODDI models in CU/MCI/AD. We then developed separate GM and WM models for each condition using samples from condition‐negative participants to compare PAG of GM and WM for that condition.ResultThere was greater PAG in GM (avg = ) than WM (avg = 9.15 yrs) in MCI/AD participants (Fig. 1A). Each condition shifted the GM PAG by 0.10 to 3.56 years and WM PAG by 0.38 to 3.72 years with stroke and congestive heart failure showing the greater PAG overall for GM and WM PAD respectively (Fig. 2). COPD, stroke, chronic kidney disease, hypertension had comparatively greater GM PAG than WM PAG; while hyperlipidemia, congestive heart failure and coronary artery disease had greater WM PAG than GM PAG (Fig. 1B).ConclusionChronic conditions are associated with increased GM and WM Brain aging models can aid in quantifying the impact of chronic conditions on dementia risk and understanding mechanisms.

AbstractBackgroundPeriodontal disease is a prevalent, preventable, and highly treatable oral infection that is (1) stratified by sociodemographic, educational, and spatial factors and (2) is believed to impact cognitive impairment and ADRD risk. However, there is very little information from community‐based, population‐representative, longitudinal studies about the association between periodontal disease and midlife cognitive functioning net of early life confounders.MethodWe use data from the U.S. High School and Beyond (HSB) cohort study, which has followed a nationally representative sample of ∼25,500 people from high school in 1980 through midlife in 2021‐22. HSB data from the 1980s includes rich information about educational contexts, opportunities, and outcomes; early life socioeconomic and family circumstances; spatial location; and demographic group memberships. HSB data from 2021‐22 include indicators of periodontal disease diagnosis and multiple measures of cognitive functioning.ResultPeople who have been diagnosed with periodontal disease score worse on measures of memory, executive function, and other domains of cognitive functioning at midlife. These associations are only partly attributable to early life confounders. Some ─ but not all ─ of the association can be attributed to the mediating role of diabetes and CVD.ConclusionPeriodontal disease is a preventable and treatable risk factor for subsequent cognitive impairment. Because risk of periodontal disease is stratified by demographic, educational, and other social factors, periodontal disease may also be important in shaping sociodemographic gradients in midlife cognitive functioning. This is the first population‐representative study using data from a community‐based cohort that has been followed from adolescence through midlife. As such, this work provides important evidence about the role of periodontal disease in stratifying cognitive outcomes.