Citations

Abstract INTRODUCTION Understanding comorbidities’ combined impacts on dementia risk may offer a more comprehensive understanding of individuals’ risk. Using machine‐learning, we grouped individuals with similar midlife risk profiles into clusters and explored associations with dementia risk. METHODS Participants without dementia at baseline (1987–1989) from the prospective Atherosclerosis Risk in Communities (ARIC) study were included (ages 45–64 years; N = 15,250). Using unsupervised hierarchical cluster analysis, nine clusters were created and defined based on 14 midlife morbidities. The associations with incident dementia ( N = 3272 cases, median follow‐up 25 years) and deaths ( N = 9099) were evaluated using time‐to‐event regression models. RESULTS Compared with the healthiest cluster (Cluster 1), Clusters 2 (smoking) (hazard ratio [HR](95% confidence interval [CI]) = 1.62 (1.08, 2.43)), 5 (obesity, diabetes, hypertension, and hypertriglyceridemia) (HR(95%CI) = 1.91 (1.35,2.70)), and 7/8 (atrial fibrillation/heart failure) (HR(95%CI) = 2.69 (1.59,4.57)) were associated with dementia. Accounting for competing risk of death in the Fine‐Gray subdistribution model negated the cluster‐dementia association. DISCUSSION Midlife morbidity clusters are important for dementia and mortality risk.

Abstract Measurement of D-dimer, a fibrin degradation product, is crucial for diagnosing thrombotic and fibrinolytic conditions. Despite its expanding utility, D-dimer testing faces challenges due to varying assay methodologies and nonstandardized reporting. To understand D-dimer reporting practices, the College of American Pathologists Hemostasis and Thrombosis Committee analyzed proficiency testing data from D-dimer surveys conducted between 2020 and 2023 across multiple laboratories. The data demonstrate that laboratories commonly report units that differ from the assay package insert when reporting their proficiency testing results. We sought to assess variability in reporting practices and opportunities for improvement in D-dimer reporting amongst participants. To accomplish this, we harmonized all D-dimer results to ng/mL fibrinogen equivalent units from one distributed sample and assessed distinct populations of entries per instrument/reagent combination. Notable trends emerged from this analysis. Data from laboratories using the same instrument/reagent combinations clustered around different means, suggesting issues with unit reporting and/or conversion. Entries reported in mg/L or ug/mL D-dimer units commonly exhibited significant deviations from the dominant population. For one instrument/reagent combination, most reported values differed by approximately twice the certificate of analysis value. This analysis highlights potential issues with D-dimer reporting in proficiency testing. Whether these issues translate into issues with clinical D-dimer reporting requires further study.

Abstract Introduction Cardiac arrest (CA) is the sudden cessation of cardiac activity, leading to haemodynamic collapse and high mortality. Advanced life support (ALS) standardises resuscitation, but the post-return of spontaneous circulation (ROSC) period remains turbulent and poorly understood. Up to 50% of patients who achieve ROSC struggle to maintain spontaneous circulation, leading to re-arrest, typically within minutes. Post-cardiac arrest syndrome (PCAS), involving brain injury and ischaemia-reperfusion injury, plays a key role in re-arrest. Pressure-volume loop (PVL) analysis offers insight into load-independent cardiac biomechanics in the post-ROSC period. The aim of this study is to explore the feasibility of PVL analysis in a swine model of CA. Methods This swine study utilised a ventricular fibrillation (VF) cardiac arrest model in combination with PVL analysis. The experiment included four phases: animal preparation, VF induction, resuscitation, and post-ROSC care. Yorkshire swine (45-70 kg) were sedated, anaesthetised, and VF was induced via an endocavitary electrode attached to a battery. After that, the animals were divided into 3-minute and 6-minute arrest groups before commencing ALS. Standard ALS protocols were followed, including CPR, defibrillation, and drug administration. If ROSC was obtained, animals underwent a 3-hour critical care period. Cardiac indices were compared between baseline and end-of-study values. Results Eight adult Yorkshire swine were enrolled in the study, with a mean weight of 50.2 ± 2.9 kg. VF induction was successfully achieved, and PVL data were collected in all animals except one, establishing a successful porcine VF CA model. In the resuscitation phase, all animals in the 3-minute group achieved ROSC, as opposed to one in the 6-minute group. Post-ROSC metabolic changes included acidaemia, elevated lactate and potassium, partially resolving by study end. Right ventricle PVL data were unreliable, while left ventricle PVL was reliable in 3 of 5 ROSC animals. Preload-recruitable stroke work (PRSW) data were found to be more reliable than the End-Diastolic Pressure-Volume Relationship (ESPVR), with a significant increase in LV PRSW of 34 ± 12% observed post-ROSC in all surviving animals (p<0.001). Discussion This study demonstrates the feasibility of applying PVL analysis to a post-ROSC swine model of cardiac arrest (CA). As expected, warm ischaemic time predicted ROSC, but PVL analysis offered valuable insights into load-independent parameters. While data quality issues hindered ESPVR analysis, PRSW was successfully evaluated, revealing a significant increase post-ROSC, highlighting its potential to guide the development of future therapeutic targets for post-ROSC interventions. The small sample size and technical challenges limited the scope of conclusions, emphasising the need for larger studies.Kaplan-Meier Curve and cycles to ROSC (A) LV PVL, (B) PRSW Pre and Post-ROSC

Introduction: Plasma biomarkers of Alzheimer’s disease (AD), including amyloid-β (Aβ42/Aβ40), phosphorylated tau (p-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) have shown strong reliability in predicting dementia risk. White matter hyperintensities (WMH) represent a major vascular contributor to dementia. We investigated how plasma biomarkers of AD interact with WMH on MRI to influence incident dementia risk. Methods: We included dementia-free participants from the Atherosclerosis Risk in Communities (ARIC) Study who underwent MRI and plasma biomarker assessment at Visit 5 (2011–2013). WMH was quantified and considered continuously (log-transformed and standardized), and in tertiles. Plasma biomarkers were log-base-2-transformed. Cox proportional hazards models were used to estimate dementia risk over 10 years associated with plasma biomarkers and WMH, independently and multiplicatively. Models were adjusted for demographic variables (age, sex, race, and education), vascular risk factors (hypertension, diabetes, smoking status, body mass index, and estimated glomerular filtration rate), and APOE ε4 status. Results: Among 1,538 participants (aged 67- 90 years, 29% Black), 30% ( N = 462) developed dementia over the 10-year follow-up period. For every doubling of plasma Aβ42/Aβ40 , indicating lower amyloid burden, dementia risk decreased, while doubling of p-tau 181 , NfL, and GFAP were each associated with increased dementia risk, in separate models; each of these associations was independent of WMH (Table 1). Additionally, dementia risk increased per SD increase in WMH, independent of each plasma biomarker. Although no multiplicative interaction terms reached statistical significance (p<0.05), there was suggestive evidence that Aβ42/Aβ40 and GFAP have smaller effect sizes on dementia risk in subgroups at the highest WMH tertile (p-interactions<0.10; see Table 2). Discussion: Plasma biomarkers and WMH independently predict dementia risk over 10 years, though there remained limited evidence of multiplicative effects. Plasma biomarkers may be less important for dementia risk in individuals who also have extensive WMH. These findings emphasize the importance of incorporating both AD-specific plasma markers as well as indicators of cerebral small vessel disease, such as WMH, into dementia risk profiling.

Abstract Background Plasma biomarkers of Alzheimer's disease (AD) pathology and neurodegeneration provide a non‐invasive method to assess brain pathology associated with dementia and cognitive decline. However, it remains unclear whether these biomarkers are differentially associated with changes in specific cognitive domains or how they relate to subsequent cognitive change in participants with or without cognitive impairment. Methods Plasma biomarkers were assayed in a subset of ARIC participants using Quanterix SiMoA technology on stored specimens collected in midlife (visit 3: 1993‐95, mean age: 59) and late‐life (visit 5: 2011‐13, mean age: 77). Biomarkers included amyloid‐β 42 to amyloid‐β 40 (Aβ42:Aβ40) ratio, phosphorylated tau at threonine 181 ( p ‐Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Factor scores for global cognition and cognitive domains (memory, language, executive function) were calculated from ten neuropsychological tests administered at visits 5 (2011‐13), 6 (2016‐17), 7 (2018‐19), 8 (2020), and 9 (2021‐22). Linear mixed effects models with multiple imputation of pre‐death cognitive change estimated the association between biomarkers in midlife and late‐life, and biomarker change from midlife to late‐life, with 5‐year change in late‐life global and domain‐specific cognition, adjusting for demographics, lifestyle and cardiovascular risk factors, and APOE‐ε4 status. Exploratory analyses examined effect modification of the biomarker‐global cognition associations by visit 5 adjudicated cognitive impairment. Results Among 1515 participants with plasma biomarkers (60.7% female, 25.5% identified as Black), 39% had late‐life cognitive impairment (Table 1). Higher midlife NfL and GFAP were associated with faster 5‐year declines in global cognition and language during late‐life (Table 2). Only midlife NfL was associated with faster late‐life memory decline. No midlife biomarkers were associated with executive function. In late‐life, lower Aβ42:Aβ40 and higher p ‐Tau 181, NfL, and GFAP were associated with faster declines in global cognition, memory, and language. Midlife to late‐life changes in p ‐Tau 181, NfL, and GFAP were associated with declines in global cognition and language. No consistent effect modification by cognitive impairment was observed (Figure 1). Conclusions Plasma biomarkers of AD pathology, neuronal injury, and astrogliosis, measured at different life epochs (midlife vs. late‐life) show distinct association patterns with subsequent changes in cognitive function across domains.

Abstract Background Plasma biomarkers show promise as a noninvasive method for measuring Alzheimer's disease pathology and neurodegeneration throughout the lifecourse. However, the relationship between longitudinal changes in these biomarkers and late‐life brain morphology in community‐dwelling populations requires further investigation. Method Between 2011 and 2013, 1,977 participants from the Atherosclerosis Risk in Communities Study received an adjudicated cognitive diagnosis and underwent 3 Tesla magnetic resonance imaging scans. Whole‐brain cortical thickness was quantified by Freesurfer. Stored plasma samples collected in midlife (1993‐95, mean age 59.0 years) and late‐life (2011‐13, mean age 76.8 years) from a subsample of 1,515 participants were assayed in 2022 using Quanterix SiMoA. The assay quantified amyloid‐β (Aβ)42/40, phosphorylated tau at threonine 181 ( p ‐Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Linear regression models estimated the association of plasma biomarkers from midlife, late‐life, and the change from midlife to late‐life with cortical thickness in late‐life. Interactions and effect modification by age were examined in exploratory analyses. Result The sample (Table 1) included 920 (60.7%) women, 387 (25.5%) Black participants, 513 (33.9%) participants with mild cognitive impairment, and 85 (5.6%) participants with dementia. The average age in late‐life was 76.8 (5.3 SD) years and the average cortical thickness was 2.27 (0.11 SD) millimeters. Cortical thickness was lower in individuals with adjudicated mild cognitive impairment or dementia compared to individuals without cognitive impairment (2.25 mm versus 2.29 mm, p <0.0001). In models adjusted for demographics, lifestyle factors, cardiovascular factors, and the presence of APOE ε4 alleles, higher midlife measures of p ‐Tau181 and increases from midlife to late‐life in NfL and GFAP were associated with lower cortical thickness in late‐life (Table 2). Concurrent measures of plasma biomarkers in late‐life were associated with lower cortical thickness (Table 2) and exhibited a stronger association among participants ≥75 years compared to <75 years (Table 3). Conclusion Plasma biomarkers of Alzheimer's disease pathology and neurodegeneration obtained in midlife and late‐life were associated with late‐life differences in cortical thickness. Additional longitudinal research is needed to determine whether plasma biomarkers predict cortical thinning over time and may serve as an early indicator of accelerated brain atrophy.

Abstract Blood-based biomarkers show promise as a noninvasive method for measuring Alzheimer’s disease pathology and neurodegeneration throughout the lifecourse. However, the relationship between longitudinal changes in these biomarkers and late-life brain morphology in community-dwelling populations requires further investigation. Between 2011 and 2013, 1,977 participants from the Atherosclerosis Risk in Communities Study received underwent 3 Tesla magnetic resonance imaging scans. Whole-brain cortical thickness was quantified by Freesurfer. Stored plasma samples collected in midlife (1993-95, mean age 59.0 years) and late-life (2011-13, mean age 76.8 years) from a subsample of 1,515 participants (60.7% female, 25.5% Black adults) were assayed in 2022 using Quanterix SiMoA. The assay quantified amyloid-β (Aβ)42/40, phosphorylated tau at threonine 181 (p-Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Linear regression models estimated the association of blood-based biomarkers from midlife, late-life, and the change from midlife to late-life with cortical thickness in late-life. In models adjusted for demographics, lifestyle factors, cardiovascular factors, and the presence of APOE ε4 alleles, higher midlife measures of p-Tau181 and increases from midlife to late-life in NfL and GFAP were associated with lower cortical thickness in late-life. Concurrent measures of p-Tau181, NfL, and GFAP were associated with lower cortical thickness in late-life and exhibited a stronger association among participants ≥75 years old compared to participants &lt;75 years old. Additional longitudinal research is needed to determine whether blood-based biomarkers predict cortical thinning over time and may serve as an early indicator of accelerated brain atrophy.

Abstract Background Even among cognitively unimpaired (CU) older adults, specific patterns of neuropsychological performance impart differential risk for progression to Alzheimer's disease and related dementias (ADRD). Early identification in the prodromal phase may inform early intervention efforts. We used a data‐driven approach to classify neuropsychological phenotypes within CU older adults from the Atherosclerosis Risk in Communities (ARIC) study and tested for differences in ADRD and cerebrovascular outcomes. Method Participants included 4737 CU (via a previously published algorithm plus consensus diagnosis protocol) older adults (mean age=75.1 years, 60.8% female, 23.1% Black) with neuropsychological data from Visit 5 (2011‐2013) of the ARIC study. Latent profile analysis was leveraged to identify unique cognitive phenotypes using 10 demographically adjusted (age, sex/gender, education, race‐center, WRAT Reading) neuropsychological z‐scores. Cognitive phenotypes were then compared on demographics, depressive symptoms, and vascular risk factors (VRFs). In a subset with available neuroimaging and biofluid data ( n range=1102‐1678), white matter hyperintensity (WMH) and bilateral hippocampal (HV) volumes normalized for intracranial volume and plasma biomarkers (i.e., Aβ42/40, ptau181, NfL, and GFAP) were examined by phenotype. Phenotype comparisons adjusted for demographic variables, VRFs, and an indicator of kidney function (i.e., eGFR‐creatinine) for plasma biomarkers. We applied Tukey adjustment for pairwise comparisons. Result A 6‐class solution of cognitive phenotypes emerged as the best fitting model: Average Overall (28%), Low Overall/Very Low Memory (7%), Average Overall/Super Memory (11%), Subtle Low Overall/Low Executive (22%), Subtle Low Executive/High Memory (13%), and Average Overall/Low Memory (19%; Figure 1). Phenotypes differed on education, race, and word reading, despite prior adjustments for these variables when creating the neuropsychological z‐scores (Table 1). Additionally, phenotypes differed on depressive symptoms, VRFs, WMH, HV, Aβ42/40, and ptau181 (all p s < .05). The Subtle Low Overall/Low Executive group had the highest WMH volumes, the Subtle Low Executive/High Memory group had the lowest Aβ42/40, and the Low Overall/Very Low Memory group had the lowest HV and highest levels of plasma ptau181 (Table 2). Conclusion Cognitive heterogeneity is observed even among CU older adults, with lower executive performance associated with cerebrovascular changes and lower memory associated with certain AD‐related changes, possibly indicating risk for cognitive progression through distinct pathways.

Abstract Background The amygdala is closely connected to the olfactory bulb and involved in olfactory processing as well. It is a particularly susceptible region that accumulates a‐synuclein pathology in Parkinson's disease (PD). Our previous work revealed olfactory bulb gene alterations related to neuroinflammation, and neurotransmitter dysfunction to be associated with olfactory function. We aimed to extend this work and assess gene expression changes, affected pathways and co‐expression networks by transcriptomic profiling of the amygdala in subjects with and without clinicopathologically defined PD. Method Bulk RNA sequencing was performed on frozen human amygdala of 20 PD and 20 controls without dementia or any other neurodegenerative disorder, from the Arizona Study of Aging and Neurodegenerative Disorders. Result Differential expression analysis, corrected for age, sex and post‐mortem interval revealed 40 significantly differentially expressed genes (DEGs) in PD. Downregulated genes were mainly related to altered hemoglobin expression while upregulated genes included inflammatory markers and glutamine transporter. Significantly enriched pathways were involved in oxygen and carbon dioxide transport, cellular oxidant detoxification and regulation of glutamine secretion. Cell enrichment revealed these genes to be mainly expressed in neurons. Co‐expression network analysis using Weighted correlation network analysis (WGCNA ) subsequently identified four significant modules correlated with both PD and premortem olfactory dysfunction which were involved in pathways related to neuroinflammatory processes, mitochondrial, endoplasmic reticulum and lysosomal dysfunction. Conclusion These preliminary results suggest cellular alterations in the amygdala related to mitochondrial dysfunction, altered oxygen homeostasis, oxidative stress, inflammation as well as glutamatergic neurotransmitter dysfunction that may contribute to neurodegeneration in PD.