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INTRODUCTION: Published norms are typically cross‐sectional and often are not sensitive to preclinical cognitive changes due to dementia. We developed and validated demographically adjusted cross‐sectional and longitudinal normative standards using harmonized outcomes from two Alzheimer's disease (AD) risk‐enriched cohorts. METHODS: Data from the Wisconsin Registry for Alzheimer's Prevention and the Wisconsin Alzheimer's Disease Research Center were combined. Quantile regression was used to develop unconditional (cross‐sectional) and conditional (longitudinal) normative standards for 18 outcomes using data from cognitively unimpaired participants (N = 1390; mean follow‐up = 9.25 years). Validity analyses (N = 2456) examined relationships between percentile scores (centiles), consensus‐based cognitive statuses, and AD biomarker levels. RESULTS: Unconditional and conditional centiles were lower in those with consensus‐based impairment or biomarker positivity. Similarly, quantitative biomarker levels were higher in those whose centiles suggested decline. DISCUSSION: This study presents normative standards for cognitive measures sensitive to pre‐clinical changes. Future directions will investigate potential clinical applications of longitudinal normative standards. Highlights: Quantile regression was used to construct longitudinal norms for cognitive tests.Poorer percentile scores were related to concurrent diagnosis and Alzheimer's disease biomarkers.A ShinyApp was built to display test scores and norms and flag low performance. [ABSTRACT FROM AUTHOR]

Background: Synaptic dysfunction occurs prior to neuronal loss in the early stages of AD. A biomarker reflective of these changes could indicate AD before clinical manifestations. SNAP‐25 is a potential candidate, but the relationship between SNAP‐25 and established AD biomarkers is unclear. Further investigation is needed to determine the utility of this biomarker during the pre‐clinical phase. Method: SNAP‐25 was measured using the Quanterix Single molecule array platform. A partial validation was conducted to evaluate assay performance in CSF and EDTA plasma. A cross‐sectional pilot study was then performed by measuring SNAP‐25 in CSF from 54 participants in the Wisconsin Registry for Alzheimer's Prevention (WRAP) and Wisconsin Alzheimer's Disease Research Center (WI‐ADRC) cohorts. SV2A synaptic density was measured in each participant using [11C]‐UCB‐J‐PET. Amyloid and tau status was determined in a subset of the group using [11C]‐PiB (N = 52) and [18F]‐MK6240‐PET (N = 44), respectively. Additional CSF biomarkers (Aß42, pTau‐181, and tTau) were measured using the Roche Cobas e601 Elecsys platform. A confirmatory study was performed with 80 additional WRAP and WI‐ADRC participants (53A+, 27 A‐). These participants did not have [11C]‐UCB‐J‐PET imaging. Result: The SNAP‐25 assay demonstrated excellent precision (<5% CV), recovery (96‐101%), and linearity in CSF. Plasma SNAP‐25 levels were detectable but inconsistent, suggesting matrix interference. In the pilot study, SNAP‐25 was highly correlated with CSF pTau‐181 and tTau (Figures 1A, 1B). SNAP‐25 was not correlated with CSF Aß42 (Figure 1C). A modest but significant correlation was observed with amyloid PET in the pilot study (Figure 2A) but not in the confirmatory study (Figure 3). SNAP‐25 did not correlate with SV2A density (Figure 2B) or fibrillary tau (Figure 2A). Mean SNAP‐25 levels were significantly different between amyloid‐ and tau‐positive groups compared to biomarker negative controls (p < 0.001 for amyloid, p < 0.05 for tau). Similar trends were observed in the confirmatory study (Figure 3). Conclusion: These results demonstrate a close relationship between SNAP‐25 and soluble tau in CSF, suggesting that CSF SNAP‐25 reflects early Aß‐induced neuronal impairment in a way that correlates with tau pathophysiology. This indicates that SNAP‐25 could be a useful biomarker of early neurodegeneration in pre‐clinical AD. [ABSTRACT FROM AUTHOR]

Background: Understanding how representative a research sample is compared to the population can help inform future recruitment planning, data analysis strategies, and interpretation of the generalizability of the findings. We compared the demographics, rural residence, neighborhood socioeconomic disadvantage, and vascular risk factors between the Wisconsin Alzheimer's Disease Research Center (ADRC) Clinical Core participants versus the Wisconsin state adult population. Method: The ADRC participant sample (n = 678) included all active participants as of November 30, 2021. The state population statistics were estimated using multiple national and state data sources. Differences between the sample versus the population were tested using the one‐sample z‐test, the one‐sample Wilcoxon signed rank test, the exact binomial test, and the exact Monte Carlo multinomial test. Except for age, comparisons were performed for two age groups: 45‐64 years and ≥ 65 years, separately. Result: The ADRC participants are older than the state population. For each age group, compared to the state population, the ADRC participants include more women and greater proportions of Black and American Indians, have higher levels of educational attainment, have smaller percentages living in rural areas and more disadvantaged neighborhoods, and show lower vascular risks. The sample versus population differences are greater for the ≥ 65 years group (the group at higher risk for AD) than the 45‐64 years group for most metrics, however, are smaller for sex and neighborhood disadvantage. Conclusion: The ADRC targets recruiting middle‐aged and older adults with dementia, mild cognitive impairment, or increased risk for AD. Thus, the participants are not a suitable sample for epidemiology studies on AD prevalence. Oversampling underrepresented racial groups would alleviate the lack of statistical power associated with small or unbalanced sample sizes and increase the precision in effect size estimates for studies on racial disparities. Future recruitment should endeavor to include participants from a broader range of educational backgrounds and more residents in rural areas and disadvantaged neighborhoods. These subpopulations have inequitable access to quality health care and are at greater risk. Properly representing them in research would allow conclusions to be generalizable to these subpopulations and support policy making to meet their needs. [ABSTRACT FROM AUTHOR]

Background: Recent research linking vascular risk with promotion of tau pathology in the inferior temporal cortex (ITC) suggests a potential mechanism connecting cerebrovascular risk to AD pathology and cognitive decline among cognitively unimpaired (CU) individuals (Yau et al, 2022).We used MRI White Matter Hyperintensity (WMH), a neuroimaging marker of chronic cerebral small‐vessel disease/cerebrovascular disease, to further elucidate longitudinal relationships between cerebrovascular risk, PET AD pathological burden, and cognition. Method: We examined baseline CU late middle‐aged adults from the University of Wisconsin's Alzheimer's disease research cohorts. β‐Amyloid burden was measured with 11C‐Pittsburgh compound B ("PiB"; DVR derived from a 0‐70 min dynamic PET scan; PET A+ threshold∼1.16 DVR/∼CL 17). Cerebrovascular risk was quantified using WMH volumes (T2‐FLAIR images; SPM Lesion Segmentation Tool). Tau burden was measured with florquinitau PET (MK6240 SUVR from 70‐90 min post‐injection). Tau outcomes included bilateral volume‐weighted averages for ITC, Medial temporal lobe (MTL) and Meta‐temporal (MTC) composites. Biomarker values were z‐transformed relative to CU/PET A‐ values. Random intercept models examined interactive effects of baseline WMH and Aβ levels on longitudinal tau (covarying baseline tau age, sex, APOE ε4 count). We modeled time two ways: years since baseline PiB (TimePiB1; replicating Yau, et al models); and, in parallel models, amyloid duration (years) at tau scans was substituted for years since PiB1 in the A+ subset (TimePiB1/PiB+). We also used LME to examine Aβ, WMH, and time interactions in longitudinal cognition (PACC3 and Digit Symbol Substitution). Result: The mean(SD) tau baseline age was 67.9(6.8) years (sample details in Table1). The most parsimonious models using TimePiB1 included WMH*Aβ*Time for ITC and MTC and WMH*Aβ for MTL. Patterns showed a synergistic effect of WMH and Aβ on tau accumulation that was attenuated using TimePiB1/PiB+ where the most parsimonious models included Aβ_status*TimePiB1/PiB+ (ITC, MTC) or WMH*Aβ_status*TimePiB1/PiB+ (MTL; Figure1). AICc model fits were substantially better using TimePiB1/PiB+. Both Aβ and WMH contributed to cognitive trajectories (Figure2). Conclusion: Although cerebrovascular risk's apparent contribution to tau accumulation varied depending on how time was modeled, results suggest that cerebrovascular risk may complement imaging biomarkers in assessing risk of tau accumulation and cognitive decline in preclinical Alzheimer disease. [ABSTRACT FROM AUTHOR]

Background: Lack of practice effects (PE) in common cognitive tests has been associated with increased risk of dementia. Previous studies have found connections between recall of proper names (PN) and amyloid positivity in cognitively unimpaired adults at risk for Alzheimer's Disease (AD; Mueller et al., 2020). For several language‐related tests, including PN, we compared PE modeling options and then examined whether amyloid status moderated PE. Method: Participants with language tasks and C‐Pittsburgh Compound‐B (PiB) amyloid positron emission tomography (PET) were selected from the Wisconsin Registry for Alzheimer's Prevention (WRAP; n = 377). PE operationalizations included: number of language task exposures – 1 ("PEnvis‐1"), the square root of PEnvis‐1 ("PEsqrt") and categorical PE ("PEcat"; reference = 0 (baseline) vs 1, 2, or 3+ follow‐up assessments). Language outcomes variables included letter fluency (CFL), animal naming, and logical memory (LM). From LM, we utilized item‐level data to create immediate and delayed PN recall. We used linear mixed effects models to identify the best fitting PE operationalization for each outcome and ran subsequent models examining amyloid*PE interactions (covariates: age at assessment, gender, and WRAT‐III reading score; random effects for intercept). Result: Sample characteristics are shown in Table 1 overall and by amyloid status. AIC fit statistics were best overall for PEnvis‐1. In subsequent models, the PE*amyloid interaction was significant for LM Total Score (β = ‐0.08, 95% CI = (‐0.15, ‐0.01), p = 0.02) and PN immediate recall (β = ‐0.22, 95% CI = (‐0.38, ‐0.05), p = 0.01). For both outcomes, predicted Z‐scores increased with more practice for amyloid positive participants, but decreased for amyloid negative participants (Table 2; Figure 1). The main effect of PE was significant for CFL (β = 0.12, 95% CI = (0.08, 0.15 p<0.001) and Animal Naming (β = 0.07, 95% CI = (0.03‐0.11), p = 0.002). Conclusion: PEnvis‐1 was generally the best‐fitting PE operationalization. In our relatively young sample (baseline mean age = 54.1; mean years between baseline and most recent LM = 9.12), improvements with practice for animal naming and CFL were not modified by amyloid status. In contrast, those with amyloid negative biomarkers showed practice‐related improvements on LM while those with positive amyloid showed decreased performance with more practice. [ABSTRACT FROM AUTHOR]

Background: Vascular dysfunction often occurs concurrently with Alzheimer's disease (AD). Breakdown of the blood–brain barrier (BBB) and vascular injury may be related to amyloid and tau pathology. In preliminary analyses, we examined the relationship between cerebrospinal fluid (CSF) biomarkers of AD, neurodegeneration, and glial activation, and two novel CSF markers of vascular dysfunction: soluble platelet‐derived growth factor receptor beta (sPDGFRβ) a marker of BBB integrity, and angiopoietin‐2 (Ang2), a marker of vascular injury. Method: CSF samples from participants enrolled in the Wisconsin Registry for Alzheimer's Prevention (WRAP) or the Wisconsin Alzheimer's Disease Research Center (WADRC) studies were assayed for markers of AD, neurodegeneration, and gliosis, using Roche NeuroToolKit® immunoassays (Roche Diagnostics International Ltd, Switzerland). A subset of serially sampled participants (N = 209, Nobs = 531) who spanned the AD clinical spectrum (cognitively unimpaired, MCI, dementia) underwent sPDGFRβ measurement (ELISA); a sample of N = 121 (Nobs= 276) was also assayed for Ang2 (ELISA) (Table 1). Pearson correlations were calculated for all CSF biomarkers. Linear mixed‐effects models with random intercepts, age‐at‐lumbar‐puncture as the measure of time, and CSF vascular biomarker as the outcome were used to test associations with tau positivity (>24.8 pg/mL), and cognitive status. Result: sPDGFRβ correlated with all CSF biomarkers (rs range.18 to.45, ps<.001, Figure 1A) except AB42/40. Ang2 correlated with all CSF biomarkers (rs range.21 to.45 ps <.001, Figure 1B) except AB42/40, and S100B. sPDGFRβ and Ang2 correlated most strongly with Aβ40 and biomarkers of synaptic function (neurogranin and α‐synuclein). Tau positivity was associated with sPDGFRβ in the whole sample (estimate = 64.5, p<.001) and excluding participants with MCI/dementia (estimate = 77.7, p<.001). Tau positivity was associated with Ang2 (estimate = 18.1, p =.007), however the association was not significant after excluding participants with MCI/dementia. There was no difference in sPDGFRβ across the AD clinical spectrum. Ang2 was higher among participants with MCI than cognitively unimpaired participants (estimate = 21.0, p =.037, Figure 2). Conclusion: CSF markers of vascular injury were elevated in MCI and were moderately related to tau pathology and markers of neuronal injury and neuroinflammation, but not amyloid pathology, providing insight into the association of of vascular injury to AD related disease [ABSTRACT FROM AUTHOR]

INTRODUCTION: A hallmark of Alzheimer's disease (AD) is the aggregation of proteins (amyloid beta [A] and hyperphosphorylated tau [T]) in the brain, making cerebrospinal fluid (CSF) proteins of particular interest. METHODS: We conducted a CSF proteome‐wide analysis among participants of varying AT pathology (n = 137 participants; 915 proteins) with nine CSF biomarkers of neurodegeneration and neuroinflammation. RESULTS: We identified 61 proteins significantly associated with the AT category (P < 5.46 × 10−5) and 636 significant protein‐biomarker associations (P < 6.07 × 10−6). Proteins from glucose and carbon metabolism pathways were enriched among amyloid‐ and tau‐associated proteins, including malate dehydrogenase and aldolase A, whose associations with tau were replicated in an independent cohort (n = 717). CSF metabolomics identified and replicated an association of succinylcarnitine with phosphorylated tau and other biomarkers. DISCUSSION: These results implicate glucose and carbon metabolic dysregulation and increased CSF succinylcarnitine levels with amyloid and tau pathology in AD. Highlights: Cerebrospinal fluid (CSF) proteome enriched for extracellular, neuronal, immune, and protein processing.Glucose/carbon metabolic pathways enriched among amyloid/tau‐associated proteins.Key glucose/carbon metabolism protein associations independently replicated.CSF proteome outperformed other omics data in predicting amyloid/tau positivity.CSF metabolomics identified and replicated a succinylcarnitine–phosphorylated tau association. [ABSTRACT FROM AUTHOR]

Background: Amyloid‐PET and white matter hyperintensities (WMH) associations in older adults have variously been interpreted as separate or dependent processes. In this study, we examine evidence for a temporal relationship between amyloid‐PET and WMH and investigate how both contribute to cognitive decline in a baseline cognitively unimpaired (CU) sample. Method: We analyzed longitudinal WMH and PACC3 cognition in n = 240 Wisconsin Registry for Alzheimer's Prevention participants who were CU at cognitive baseline with ≥1 amyloid‐PET and T2‐FLAIR scan. Amyloid burden was assessed using [C‐11]Pittsburgh Compound B (PiB) PET and a multi‐ROI DVR value; DVR>1.19 = PiB+. WMH lesion volume was segmented and summed by the Lesion Segmentation Tool (LST) version 1.2.3 in SPM12 from T1‐weighted and T2‐weighted FLAIR scans; TIV‐adjusted WMH >2.06 = WMH+. Sampled iterative local approximation (SILA) modeling aligned biomarker levels to positivity duration scale (years = 0 at biomarker+ threshold). To investigate how amyloid burden relates to WMH trajectories, time at each WMH scan was operationalized four ways: age, ∼WMH+ duration, years_since_baseline_scan, and ∼PiB+ duration. We compared model fits and interaction effects for the various PiB status*time interactions (mixed effects models; retaining significant quadratic time terms; random slopes and intercepts). We also examined whether PACC3 age‐related trajectories differed by PiB/WMH +/‐ groups (at last scan). Result: More women were WMH+ and APOE4 was more common among PiB+ (see also Table1). Significant interactions using age or years_since_baseline_scan suggested slightly faster WMH accumulation in PiB+ vs PiB‐ at older ages. In parallel models, WMH+ duration*PiB status and PiB+ duration*PiB status did not show differences between PiB+ and PiB‐ WMH trajectories. Time operationalized as WMH+ duration yielded the best fitting model (see Table2 and Fig1A‐D). PACC3 decline was fastest in the PiB+/WMH+ group, followed by PiB+/WMH‐ (Fig1E). PiB+ was more common than WMH+ and tended to precede it when both were present (Table1; Fig1F). Conclusion: In this baseline CU sample, WMH+ was less common and occurred later in life, on average, than PiB+. Associations between amyloid burden and WMH trajectories were driven by older ages, suggesting potential unidentified age‐related mediators in this relationship. Presence of both amyloid and WMH was associated with fastest preclinical cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Lower measures of myelin have been associated with abnormal levels of AD biomarkers and APOE4 carriage. However, most human studies have been cross‐sectional, leaving the relationship between AD and myelin changes unclear. The purpose of this study was to determine the association between AD pathology, APOE4, glial activation, and longitudinal changes in quantitative R1 (qR1), a marker sensitive to myelination obtained via MPnRAGE MRI. Method: Participants from the Wisconsin Registry for Alzheimer's Prevention (WRAP) and Wisconsin Alzheimer's Disease Research Center (ADRC) were selected based on available longitudinal MPnRAGE, clinical diagnosis, and APOE genotyping (N = 446). A subset (N = 141) included those with available baseline CSF (AB42/40, pTau181), and gliosis markers (sTREM2, GFAP, YKL40) measured using a robust prototype assay as part of the Roche NeuroToolKit research platform (Roche International). Longitudinal mean qR1 was estimated from white matter regions from the ICBM‐DTI‐81 atlas. In the overall sample, linear mixed effects models predicting regional qR1 (FDR‐corrected) with random intercepts and slopes were estimated controlling for sex, age centered within‐subjects (agec), age averaged within‐subjects (agegm), cognitive impairment, and APOE4 carriage as covariates. Two‐way interactions between impairment and APOE4 with agec were tested in the overall sample as were moderating effects of CSF amyloid‐positivity, pTau181, and gliosis markers on agec in the CSF sample. Result: In the overall sample, older agec, agegm, and female sex were associated with lower qR1 in a majority of tracts indicating substantial longitudinal declines in qR1 over time and lower qR1 with advanced age and female sex. Sex, impairment, and APOE4 did not show significant agec interactions. Likewise, none of the CSF biomarkers moderated the effect of agec in the CSF subsample. Conclusion: While aging is robustly associated with longitudinal demyelination as potentially indexed by qR1, these rates do not appear to be moderated by AD pathology, cognitive impairment, APOE4, or glial activation in early disease. Given the capacity for re‐myelination in the context of injury, longitudinal studies are critical for parsing myelin and AD relationships. Established associations between myelin and AD risk may reflect later disease processes, particularly in samples more enriched for tau pathology. [ABSTRACT FROM AUTHOR]

Background: This work characterized longitudinal changes in cerebrovascular health in humans with and without presence of small vessel disease (SVD) and amyloid‐β using different neuroimaging techniques. Method: Participants (n = 41) were biomarker characterized into three groups: amyloid and SVD negative (A‐V‐; "control"), SVD positive (A‐V+), and amyloid positive (A+V‐). All were cognitively unimpaired except two participants clinically diagnosed with MCI (A‐V+) and dementia (A+V‐) (Table 1). MRI: Vascular imaging: Whole‐brain 4D‐Flow data were acquired to characterize cerebrovascular health. Two MRI time‐points were used to establish 4D‐Flow‐based longitudinal changes. Measurements were extracted from intracranial arteries and veins and included blood flow, vessel pulsatility index, and trans‐capillary pulse wave delay (TPWD). TPWD was defined as the time shift between arterial and venous cardiac waveforms. Structural imaging: TIV‐normalized WMH volumes were segmented from T2‐FLAIR images and used as surrogate for SVD. V+/‐ thresholds were determined from Gaussian Mixture Model on 930 participants. PET: Amyloid‐β burden was assessed using 11C‐PiB PET imaging. Amyloid positivity was established as mean cortical PiB DVR >1.16. Vascular changes were assessed using Kruskal‐Wallis followed by multiple comparisons (P<0.05 significance). Result: After ∼4.5 years, middle cerebral arteries (MCAs) blood flow decreased significantly more in amyloid than SVD groups, while vessel pulsatility increased significantly more in amyloid compared to SVD (Fig.1). TPWD shortened significantly in SVD compared to amyloid and control groups. All groups showed a decreased in blood flow, increased in vessel pulsatility and shortening of TPWD with time. At the time of the last MRI visit, MCAs blood flow was somewhat higher in SVD compared to amyloid and control groups, but not significantly (Fig.2). Vessel pulsatility was similar between groups, yet slightly higher in SVD and amyloid groups. TPWD was significantly shorter in SVD compared to the amyloid group. Conclusion: This work demonstrates the feasibility to measure longitudinal cerebrovascular changes using 4D‐Flow MRI in the context of SVD and AD biomarker positive groups. Preliminary results indicate 4D‐Flow‐based vascular markers can detect differences between SVD and amyloid groups both longitudinally and cross‐sectionally. Cerebrovascular changes in A+ suggest decreased metabolism and increased macro‐vessel stiffness, while findings in SVD positive supports stiffness of the micro‐circulation and capillaries. [ABSTRACT FROM AUTHOR]

Background: Neuronal hypometabolism measured with [18F]FDG PET scans can be used to identify neurodegeneration as part of the AT(N) (amyloid, tau, neurodegeneration) framework in AD. PET imaging of synaptic vesicle glycoprotein SV2A using [11C]UCB‐J provides an index of synaptic density, which has been shown to decrease during neurodegeneration. The purpose of this study was to examine the regional relationship between neuronal metabolism and synaptic density in participants across the continuum of AD‐related pathology. Method: Participants were recruited from the Wisconsin ADRC and the Wisconsin Registry for Alzheimer's Prevention (WRAP) study. A subset of 13 participants (Table 1) were selected who had completed cognitive testing, structural MR imaging, and PET imaging with the following: [11C]PiB centiloid (CL) for amyloid +/‐ status, [18F]MK‐6240 SUVR for tau +/‐ status, [11C]UCB‐J DVR for regional synaptic density burden, and [18F]FDG SUVR for regional glucose metabolism. UCB‐J and FDG analysis used a whole cerebellum reference region; PiB and MK‐6240 analysis used cerebellar GM reference regions. Inter‐tracer (UCB‐J/FDG) correlations were performed in Freesurfer‐defined bilateral Braak‐associated ROIs (Medial Temporal Lobe [MTL]: entorhinal cortex, hippocampus, and amygdala; Ventral Temporal Lobe [VTL]: fusiform, lingual, and inferior temporal gyri), signature regions of hypometabolism in AD (posterior cingulate, parietal, and frontal cortices) and a region with only marginal hypometabolism in AD (occipital cortex). Associations were determined using Pearson correlations (r). Result: All ROIs demonstrate a positive association between FDG SUVR and UCB‐J DVR, with r > 0.7 and p < 0.01 (Figure 1). The occipital, frontal, and posterior cingulate cortices demonstrate the strongest correlations (r = 0.84, 0.83, and 0.81, respectively). Conclusion: In cognitively impaired and unimpaired participants showing varying degrees of β‐amyloid and neurofibrillary tau burden, strong positive correlations are observed in regions associated with AD tau pathology and hypometabolism. Correlations are also observed in regions not typically associated with hypometabolism. This suggests that a relationship may exist between synaptic density and neuronal metabolism in the presence of AD neuropathology. [ABSTRACT FROM AUTHOR]

Background: The extent to which amyloid‐β burden and neurofibrillary tau tangle burden are associated with synaptic degeneration in vivo is not well known. PET imaging was used to examine the cross‐sectional relationship between neurofibrillary tau tangle burden and amyloid‐β plaque burden with synaptic density among individuals who spanned the clinical and biologic Alzheimer's disease continuum. Method: Participants were recruited from the Wisconsin Alzheimer's Disease Research Center and the Wisconsin Registry for Alzheimer's Prevention study. N = 64 participants were included in the analysis based on completed cognitive testing, structural MR and PET imaging using [11C]PiB DVR for amyloid burden, [18F]MK‐6240 SUVR for regional neurofibrillary tau burden, and [11C]UCB‐J DVR for regional synaptic density burden analysis. Amyloid burden was indexed as a global [11C]PiB DVR representing the mean of brain regions that typically accumulate amyloid. UCB‐J DVR and MK‐6240 SUVR regions of interest were selected based on the histopathological staging of tau pathology in Alzheimer's disease with a focus on regions of tau pathology described by earlier and later Braak stages. The first composite region of interest (ROI1) comprised the hippocampus and entorhinal cortex, and the second (ROI2) comprised the amygdala, fusiform, lingual gyrus, and the inferior temporal gyrus. Linear correlations (Pearson's r) tested the relationship between UCB‐J DVR and PiB DVR, as well as UCB‐J DVR and MK‐6240 SUVR. Result: Global [11C]PiB DVR showed a moderate negative relationship with UCB‐J DVR in ROI1 (r = ‐0.42, p = 0.0006) and a weak relationship with UCB‐J DVR in ROI2 (r = ‐0.33, p = 009). [18F]MK‐6240 SUVR and UCB‐J DVR were negatively correlated in ROI1 (r = ‐0.44, p = 0.0003) and in ROI2 (r = ‐0.42, p = 0.005). Conclusion: Both amyloid burden and tau pathology were associated with lower synaptic density. Tau and synaptic density appeared to show moderate correlations among brain regions involved in both earlier and more advanced tau pathology, whereas amyloid burden appeared to be more strongly related to synaptic density in brain regions where more advanced disease is typically observed. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes (T2D) and insulin resistance (IR) are associated with increased dementia risk, but their association with plasma AD biomarkers e.g., plasma phosphorylated tau (pTau) is unknown. Here, we tested the relationship between diabetes, IR, and pTau, as well as considering the influence of comorbid conditions (obesity and kidney disease) on plasma biomarker measurements. Method: 425 participants from the Wisconsin Registry for Alzheimer's Prevention were included based on availability of plasma assays. Plasma pTau181 and pTau231 were measured using a single molecule array (Simoa) assay on Quanterix. Diabetes status at baseline visit was determined using fasting blood glucose and medication usage. IR was measured using the homeostatic model assessment of IR (HOMA2‐IR) and z‐scored. Obesity was indicated through body mass index (BMI) and kidney function was determined by blood creatinine levels. We used Welch's t‐test to examine differences in pTau levels based on diabetes status. Pearson correlations tested the linear relationships between longitudinal pTau181 and pTau231 with baseline HOMA2‐IR, BMI, and creatinine. Two linear mixed effects models were fitted; one for each of the pTau markers. Each model included random intercepts and age‐related slopes with fixed effects of baseline z‐scored HOMA2‐IR, BMI, and creatinine. Results were considered significant at p<.05unadjusted. Result: Sample characteristics are in the Table. Baseline HOMA2‐IR correlated with pTau181 (r = 0.066, p = 0.021), but not pTau 231. Baseline creatinine associated with both pTau181 and pTau231 (Figure 1). Creatinine was also longitudinally associated with pTau231 (β = 4.82, p = 0.004; Figure 2). When accounting for BMI and creatinine, HOMA2‐IR was no longer associated with longitudinal pTau181 or pTau231. Age was robustly associated with pTau181 (β = 0.04, p = 6.06×10−7) and pTau231 (β = 0.15, p = 5.21×10−7) change. Plasma pTau did not differ based on diabetes status. Conclusion: IR associated with pTau181 but this relationship did not hold when adjusting for obesity and kidney function. Creatinine levels were associated with longitudinal changes in pTau231 levels. Age was strongly associated with pTau181 and pTau231 change, such that older age was associated with increasing pTau181 and pTau231 levels. Though this study was completed in a largely healthy cohort, the results suggest comorbid conditions are important considerations when utilizing plasma pTau biomarkers as primary outcomes. [ABSTRACT FROM AUTHOR]

Background: Several recent studies have provided evidence for high diagnostic performance of plasma biomarkers for AD, with Area Under the Curve values sometimes exceeding 90%. However, positive and negative predictive values are rarely reported yet are critical for interpreting individual‐level screening test results. Method: We determined positive and negative predictive values of plasma biomarkers (p‐tau181, p‐tau217, p‐tau231, GFAP and NfL) for biological AD (A+T+, determined either with PET or CSF) in the TRIAD, WRAP, ALFA+, BIODEGMAR, ADNI and McGill memory clinic cohorts (total n = 2219). We also determined positive and negative predictive values for specific clinical scenarios: MCI and mild AD – two populations that may benefit from disease‐modifying therapies in AD. Prevalence estimates of biological AD in these groups were taken from the Mayo Clinic Study of Aging. Result: P‐tau217 had the highest positive and negative predictive values of all biomarkers in all cohorts, with above 80% positive and negative predictive values. In individuals with MCI, an etiologically heterogeneous syndrome, plasma p‐tau217 had sufficiently high performance to rule out biological AD in differential diagnosis. In mild AD dementia, plasma p‐tau217 could rule in biological AD, but follow‐up with PET/CSF will likely be needed to confirm the absence of AD. Conclusion: Plasma biomarkers have the potential to be used as stand‐alone diagnostic tools in specific clinical scenarios. In MCI, a negative plasma p‐tau217 could rule out AD as a cause of cognitive impairment, but a positive result should be followed up with PET/CSF. In mild AD dementia, a positive plasma p‐tau217 result can rule in biological AD, but a negative result should be followed up with PET/CSF. [ABSTRACT FROM AUTHOR]

Background: In the last 5 years, immunoassays for the quantification of phosphorylated tau (p‐tau) in blood have proven to accurately identify Alzheimer's disease (AD) pathology with important implications for primary care management and therapeutic trials. Comparisons of p‐tau epitopes (p‐tau181, p‐tau217, p‐tau231) show small differences in diagnostic accuracy, however, often p‐tau217 is preferred, citing its larger fold‐change in symptomatic patients and lower rate of false positives in amyloid‐negative individuals. This study describes the performance of a novel Single molecule array (Simoa) for p‐tau217 (p‐tau217ALZpath) Method: We included 355 participants from Translational Biomarkers in Aging and Dementia (TRIAD) participants, which was representative of the AD continuum. Further, 425 cognitively unimpaired individuals, with longitudinal plasma measures (≤8 years), were included from Wisconsin Registry for Alzheimer's Prevention (WRAP). All plasma samples were measured for ALZpath p‐tau217ALZpath, a validated Simoa assay at the University of Gothenburg, Sweden. In TRIAD, amyloid positivity (A+) was defined as [18F]AZD‐4694 SUVR >1.5 or CSF Aβ42/40 <0.068. In WRAP, A+ was defined by [11C]PiB >1.16. 18F‐MK‐6240 determined tau status (T+) both cohorts (TRIAD, >1.24 SUVR; WRAP, >1.3). Result: In TRIAD, p‐tau217ALZpath determined A+ (AUC = 0.957, 0.935‐978) and T+ (AUC = 0.952, 0.929–0.976) individuals with high accuracy. A sensitivity analysis, including only participants with imaging (amyloid and tau) and CSF biomarkers (p‐tau181, p‐tau205, p‐tau217, Aβ42/40), p‐tau217ALZpath demonstrated equivalent accuracies in determining A+ (Figure 1A) and T+ (Figure 1B) than these high‐performing modalities. In addition, plasma p‐tau217AlzPATH demonstrated a good accuracy to identify T+ from all A+ participants (AUC = 0.839, 0.767‐910). Concentration cut‐points derived in WRAP to identify A+ (>0.676 pg/mL; PPV = 87.2%) and A– (<0.235 pg/mL; NPV = 98.2%) were directly tested in TRIAD with high accuracy (>0.676 pg/mL, PPV = 98.8%; <0.235 pg/mL, NPV = 95.1%. In WRAP, longitudinal analysis (Figure 2) demonstrated that over 8‐years, p‐tau217AlzPATH increased only in A+ individuals and more steeply in A+T+ participants (βestimate = 0.1 pg/mL per year; P<0.0001) compared to A+T‐ (βestimate = 0.022 pg/mL per year, P = 0.0008). Conclusion: The novel p‐tau217ALZpath immunoassay demonstrates high accuracy to determine amyloid and tau pathology across all stages of AD continuum. Clinical cut‐points to identify A+ or A– with high PPV and NPV are directly transferable across cohorts. [ABSTRACT FROM AUTHOR]

Background: Neuronal hypometabolism measured with [18F]FDG PET scans can be used to identify neurodegeneration as part of the AT(N) (amyloid, tau, neurodegeneration) framework in AD. PET imaging of synaptic vesicle glycoprotein SV2A using [11C]UCB‐J provides an index of synaptic density, which has been shown to decrease during neurodegeneration. The purpose of this study was to examine the regional relationship between neuronal metabolism and synaptic density in participants across the continuum of AD‐related pathology. Method: Participants were recruited from the Wisconsin ADRC and the Wisconsin Registry for Alzheimer's Prevention (WRAP) study. A subset of 13 participants (Table 1) were selected who had completed cognitive testing, structural MR imaging, and PET imaging with the following: [11C]PiB centiloid (CL) for amyloid +/‐ status, [18F]MK‐6240 SUVR for tau +/‐ status, [11C]UCB‐J DVR for regional synaptic density burden, and [18F]FDG SUVR for regional glucose metabolism. UCB‐J and FDG analysis used a whole cerebellum reference region; PiB and MK‐6240 analysis used cerebellar GM reference regions. Inter‐tracer (UCB‐J/FDG) correlations were performed in Freesurfer‐defined bilateral Braak‐associated ROIs (Medial Temporal Lobe [MTL]: entorhinal cortex, hippocampus, and amygdala; Ventral Temporal Lobe [VTL]: fusiform, lingual, and inferior temporal gyri), signature regions of hypometabolism in AD (posterior cingulate, parietal, and frontal cortices) and a region with only marginal hypometabolism in AD (occipital cortex). Associations were determined using Pearson correlations (r). Result: All ROIs demonstrate a positive association between FDG SUVR and UCB‐J DVR, with r > 0.7 and p < 0.01 (Figure 1). The occipital, frontal, and posterior cingulate cortices demonstrate the strongest correlations (r = 0.84, 0.83, and 0.81, respectively). Conclusion: In cognitively impaired and unimpaired participants showing varying degrees of ß‐amyloid and neurofibrillary tau burden, strong positive correlations are observed in regions associated with AD tau pathology and hypometabolism. Correlations are also observed in regions not typically associated with hypometabolism. This suggests that a relationship may exist between synaptic density and neuronal metabolism in the presence of AD neuropathology. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Our objective was determining the optimal combinations of cerebrospinal fluid (CSF) biomarkers for predicting disease progression in Alzheimer's disease (AD) and other neurodegenerative diseases. METHODS: We included 1,983 participants from three different cohorts with longitudinal cognitive and clinical data, and baseline CSF levels of Aβ42, Aβ40, phosphorylated tau at threonine‐181 (p‐tau), neurofilament light (NfL), neurogranin, α‐synuclein, soluble triggering receptor expressed on myeloid cells 2 (sTREM2), glial fibrillary acidic protein (GFAP), YKL‐40, S100b, and interleukin 6 (IL‐6) (Elecsys NeuroToolKit). RESULTS: Change of modified Preclinical Alzheimer's Cognitive Composite (mPACC) in cognitively unimpaired (CU) was best predicted by p‐tau/Aβ42 alone (R2 ≥ 0.31) or together with NfL (R2 = 0.25), while p‐tau/Aβ42 (R2 ≥ 0.19) was sufficient to accurately predict change of the Mini‐Mental State Examination (MMSE) in mild cognitive impairment (MCI) patients. P‐tau/Aβ42 (AUC ≥ 0.87) and p‐tau/Aβ42 together with NfL (AUC ≥ 0.75) were the best predictors of conversion to AD and all‐cause dementia, respectively. DISCUSSION: P‐tau/Aβ42 is sufficient for predicting progression in AD, with very high accuracy. Adding NfL improves the prediction of all‐cause dementia conversion and cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Earlier detection of Alzheimer's disease (AD) coupled with improved accessibility of testing means more adults may learn their biomarker results. To reduce potential negative psychosocial impacts and promote autonomy to act on the AD risk information to make lifestyle and advanced care planning changes, it is imperative that individuals understand the meaning of their results. The Wisconsin Registry for Alzheimer's Prevention is disclosing β‐amyloid (Aβ) PiB‐PET results to a subsample of cognitively unimpaired older adult participants and assessing the impact of Aβ‐PET disclosure on psychological well‐being, health behaviors, and long‐term planning. Method: Prior to learning Aβ‐PET results, participants (n = 64,age = 71.4±4.6) engaged in an education session. Participants were assessed on their baseline knowledge of AD and meaning of Aβ‐PET results utilizing a 15‐question quiz with multiple choice and true/false questions. Next, participants were guided through a 20‐minute interactive presentation describing AD and potential Aβ‐PET results. The same quiz was administered again to ensure adequate understanding. We assessed within‐person differences on pre‐ and post‐education quiz scores using paired t‐tests. The Views and Perceptions of Amyloid Imaging questionnaire surveyed reasons participants wanted to learn their Aβ‐PET results. Upon study completion, participants provided feedback on their experience and satisfaction with different elements of the education session. Result: The most common reasons participants reported wanting to learn their Aβ‐PET results included to contribute to AD research (88.9% very‐extremely important) and to inform potential preventative measures (85.2% very‐extremely important). Post‐education quiz scores were on average two points higher than baseline scores (t = 10.2, df = 62, p<0.0001). Of those that have completed 6‐month follow‐up, over 90% agreed they understood the meaning of elevated and non‐elevated results and 73% agreed the educational materials were useful. Conclusion: Even in this highly educated and research‐involved group, an education session improved knowledge about AD risk and amyloid PET results. As research further elucidates the relationship between preclinical biomarker accumulation and AD dementia risk, it will be important to effectively communicate findings with individuals who learn their AD biomarker results. Returning results can promote transparency and potentially goodwill with participants. However, responsible return of results must incorporate intentional efforts to ensure understanding of such information. [ABSTRACT FROM AUTHOR]

Background: Previous studies indicate that rates of cognitive decline in Alzheimer's disease (AD) and other dementias may accelerate significantly as brain pathology increases. The objectives of this study included to: 1) characterize changepoints and related slope parameters across four cognitive composites in a longitudinal cohort study; and 2) examine how these parameters varied across predictors of interest. Methods: Dementia‐free participants at baseline from the Wisconsin Registry for Alzheimer's Prevention (WRAP) with > = 3 cognitive assessments were eligible (n≈1071; mean(sd) baseline age = 58.4(6.4)). We used newly‐developed Bayesian random change point mixed models (BRCPMM) to estimate fixed (group‐level) and random (person‐level) change points (CPs), slopes pre‐ and post‐CP, and intercepts at CP for four cognitive composites: three‐test preclinical Alzheimer's cognitive composite (PACC3; primary outcome), immediate learning, delayed learning, and executive function. We used non‐parametric statistics to test whether PACC3 estimates differed by: last cognitive status observed (cognitively unimpaired‐stable or ‐declining (CU‐S, CU‐D), MCI, and dementia); sex/gender, and APOE‐e4 carrier status. Results: Posterior median fixed and random effect estimates (corresponding 95% credible intervals from 20000 BRCPMM runs) are summarized in Table 1 by outcome. The PACC3 group‐level CP was earliest at ∼75.56 [72.98,78.44] years old with 95% of individuals hitting CP between 59.69 and 91.86. Timing of CP was significantly associated with last observed cognitive status (Table 2); those 37 (3.46%) progressing to MCI/dementia had estimated CP's before or during study participation while those remaining unimpaired had estimated CP's after their last visit. All random effects (person‐level) PACC3 estimates differed by worst cognitive status (Figure_1) and sex/gender (Figure_2). For example, while the CU‐D and MCI groups had similar slopes before CP, CP occurred earlier, estimated PACC3 at CP was lower, and post‐CP slope was worse in the MCI group compared to CU‐D (Figure_1). APOE‐e4 random‐effects estimates differed by CP and post‐CP slopes (Figure 3). Conclusions: The timing and rates of change in different cognitive composites differ by cognitive status, gender, and APOE‐e4 status. Predicting individual cognition trajectories and change points may provide an opportunity for early intervention by diagnosing high‐risk subjects at the right time for treatment and/or enrollment in a clinical trial. [ABSTRACT FROM AUTHOR]

Background: Difficulty recalling proper names (PNs; e.g., names of people/places) is a common complaint of individuals at risk for Alzheimer's disease (AD). We recently reported that cognitively unimpaired (CU), positron emission tomography amyloid‐beta (Aβ) positive (A+) individuals recalled fewer PNs than Aβ negative individuals on the Logical Memory (LM) delayed story recall test. Declines in delayed LM total scores (LMtot) have been associated with cerebrospinal fluid (CSF) A+ and tau (T); the relationship between PNs and AD‐CSF biomarkers is unknown. We investigated whether delayed recall of PNs at baseline, or most recent visit, were associated with biomarker positivity for two AD‐CSF biomarkers, and whether PNs were more strongly associated than traditionally used LMtot scores. Method: CU participants with no other neurological diagnoses and CSF data available were included from the Wisconsin Registry for Alzheimer's Prevention (N = 219; mean baseline age [standard deviation] = 58.12 [6.31]). Item‐level LM data were used to create PNs (0–9) and LMtot (0–50) scores. A and T statuses (+/‐) were determined using published cutoffs for CSF Aβ42/40 and phosphorylated tau 181 (pTau181). Logistic regression models investigated whether LMtot or PNs were associated with most recent biomarker status, covarying CSF age, sex, Wide Range Achievement Test‐3 score, and apolipoprotein E4 status. Model fits were compared using Akaike information criterion (AIC). For descriptive purposes, effect sizes between adjusted means of PNs and LMtot were calculated for biomarker‐positive vs. negative groups. Result: Overall, 31 individuals (14%) were Aβ42/40+ and 29 (13.0%) were pTau181+ (Table 1). Baseline PNs nor LMtot were not significantly associated with AD‐CSF biomarkers (Table 2). Most recent PNs and LMtot were associated with AD‐CSF (Table 3). AICs indicated similar fits between PNs and LMtot at each timepoint, and better fits with most recent vs. baseline LM scores. Effect sizes were higher for most recent LM metrics compared to baseline and were slightly higher for PNs than LMTot for Aβ (Figure 1). Conclusion: Metrics from story recall tasks are associated with increased risk of CSF‐AD biomarker positivity and suggest that PNs from story recall may be sensitive to early AD biomarker changes. Validation by replication in additional cohorts is required. [ABSTRACT FROM AUTHOR]

Background: The degree of involvement of amyloid and tau on cognitive decline in the pre‐symptomatic phase of AD remains unresolved. This study examines whether duration of amyloid positivity and entorhinal tau individually contribute or interact to influence longitudinal cognitive decline in initially unimpaired older individuals. Method: Data were from 358 initially unimpaired individuals from the Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center who underwent amyloid and tau PET imaging and longitudinal cognitive assessment (Baseline age: 59.7 ±6.7 years; 67% female; follow‐up years, Median(IQR)=8.0 (6.1‐9.8); Table 1). Amyloid burden was quantified from cortical 11C‐PiB DVR, and amyloid duration, quantified as years from PiB positivity (A+/‐; global PiB DVR1.16), was estimated using a sampled iterative local approximation algorithm. Tau burden was quantified using 18F‐MK‐6240 standard uptake value ratios (SUVRs; 70‐90min, inferior cerebellar grey matter reference region). Linear mixed effects (LME) models (random intercept and age‐related slope; unstructured covariance) investigated contributions of amyloid duration (at MK‐6240 scan) and entorhinal cortex (EC) MK‐6240 uptake to retrospective cognitive decline represented by a modified three‐test Preclinical Alzheimer's Cognitive Composite (PACC‐3). Result: Among initially unimpaired individuals, faster PACC‐3 decline was associated with both longer amyloid duration and higher EC tau. LME models showed significant linear and non‐linear interactions of amyloid duration and EC tau on cognitive decline, suggesting a synergistic effect whereby amyloid duration together with a higher tau burden increases the likelihood of cognitive decline beyond their separable effects (Table 2). To further characterize these interactions, we compared cognitive trajectories of groups based on amyloid duration and EC tau positivity (Figure 1A‐C). Post‐hoc analyses revealed significantly greater decline in the group with 10+ years of amyloid duration and high EC tau compared with all other groups over the study duration (Figure 1C). Conclusion: These results suggest that in this pre‐symptomatic sample, amyloid duration and EC tau were not redundant contributors to decline, but rather interacted to influence decline during the retrospective period of observation. Prospective follow‐up and mediation analyses will be essential to fully elucidate the influence of amyloid duration and early tau on cognitive decline in preclinical Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: Amyloid‐β (Aβ) and tau markers have very good prognostic value for Alzheimer's disease (AD); however, novel neurodegeneration and glial‐related cerebrospinal fluid (CSF) biomarkers may help further improve prognosis. Using multiple CSF biomarkers from a single multi‐panel, we explored the combination of biomarkers that best predicts cognitive decline and clinical progression in three separate cohorts with relevance for clinical practice and trials. Method: In this study, we included 1,991 participants (cognitively unimpaired [CU], n=1,462; mild cognitive impairment [MCI], n=529) from three separate cohorts (BioFINDER‐1, BioFINDER‐2 and Wisconsin's: Wisconsin Registry for Alzheimer's Prevention [WRAP] & Wisconsin Alzheimer's Disease Research Center [WADRC]; Table 1) with longitudinal cognitive and clinical measurements. CSF was measured in all participants using the NeuroToolKit panel (a panel of automated Elecsys® and robust prototype immunoassays, Roche Diagnostics International Ltd) for biomarkers of AD pathology (p‐tau/Aβ42 ratio), neurodegeneration (neurofilament light chain [NfL], neurogranin, and α‐synuclein), and glial function (sTREM2, GFAP, YKL‐40, IL‐6, and S100b). Linear mixed models and generalized linear mixed models were used in each cohort to identify biomarker combinations most predictive of clinical progression (to Alzheimer's dementia or all‐cause dementia) or cognitive decline (Preclinical Alzheimer's cognitive composite [PACC] for CU and Mini‐Mental State Examination [MMSE] for MCI). Best performing models were compared to single‐predictor (p‐tau/Aβ42) models using Akaike information criterion (AIC) and areas under the curve (AUC), respectively. Result: Only p‐tau/Aβ42 ratio was selected as predictor in the parsimonious models for predicting conversion to Alzheimer's dementia in all cohorts and conversion to all‐cause dementia in the Wisconsin's cohort (Table 2, Figure 1). In the other two cohorts, model performance was significantly improved by the addition of NfL and neurogranin (BioFINDER‐1) and NfL alone (BioFINDER‐2). For predicting cognitive decline, parsimonious models included p‐tau/Aβ42 ratio alone (MMSE in BioFINDER‐1 and PACC in the other cohorts) or together with NfL (rest) as predictors. Conclusion: The CSF p‐tau/Aβ42 ratio is sufficient for prediction of AD progression, showing very high accuracy in three separate cohorts. The addition of NfL to p‐tau/Aβ42 can be applied to a clinical setting to further improve the prediction of all‐cause dementia and cognitive decline. [ABSTRACT FROM AUTHOR]

Background: This work characterized cerebrovascular health in human subjects spanning the cognitive spectrum with and without presence of Aβ using different neuroimaging techniques (Fig1). Methods: Subjects (n=70) were clinically characterized into four groups: cognitively unimpaired (CU), CU‐Declining (at‐risk of progressing to clinical mild MCI based on traditional neuropsychological data), clinical MCI, and clinical dementia (Fig2). MRI: Vascular imaging: Whole‐brain 4D‐Flow data were acquired to characterize cerebrovascular health. pCASL with three post labeling delays was used to provide transit time corrected CBF maps. Structural imaging: T1‐weighted and T2‐FLAIR images were also collected for quantification of intracranial, hippocampal and white matter hyperintensity volumes. Hemodynamic measurements were extracted from intracranial arteries including the petrous ICAs, BA, MCAs, and veins SSS. Quantified flow parameters included: total cerebral blood flow, trans‐capillary pulse wave delay, and flow pulsatility index (PI). Trans‐capillary pulse wave delay was defined as the time shift between arterial and venous cardiac waveforms. PET: Aβ burden was assessed using 11C‐PiB PET imaging. Amyloid positivity (A‐, A+) was established as mean cortical PiB DVR >1.16. For A+ participants, amyloid time (i.e., estimated duration A+) was estimated using the sampled iterative local approximation algorithm. Differences were assessed using ANOVA followed by post‐hoc analysis (P<0.05 significance). Results: Micro‐vascular tissue perfusion was statically higher in A+/CU when compared to other A+ groups in the GM, WM, and hippocampus (Fig3 and 4). Trans‐capillary pulse wave delay was statistically longer in A+/CU when compared to A+/CU‐Declining, A+/MCI, and A+/dementia (Fig5A). For A‐ groups trans‐capillary pulse wave delay was similar; yet, flow PI was statistically higher in A‐/CU‐Declining and A‐/MCI when compared to A‐/CU in the ICAs and MCAs (Fig5 C, D). Flow PI in A+/CU was higher than in A‐/CU. Conclusions: Decreased micro‐vascular tissue perfusion and shorter trans‐capillary pulse wave delay in A+ subjects with cognitive alterations (e.g. CU‐Declining, MCI, Dementia) compared to A+/CU indicates decreased metabolism and increased small vessel stiffness may contribute to cognitive decline and/or are induced by amyloidosis. In contrast, for A‐ subjects significant differences in arterial flow pulsatility index between A‐/CU, CU‐Declining, and MCI suggests macro‐vascular arterial stiffness contributes to cognitive decline. [ABSTRACT FROM AUTHOR]

Background: In an emerging biomarker era, scientists, clinicians and patients must trust that Alzheimer's disease (AD) biomarkers are broadly applicable across populations. However, biomarker studies inadequately include minoritized populations – largely because biomarker collection is invasive and centralized, and outreach wholly insufficient. The AA‐FAIM study collected plasma and cognitive data from a Black cohort by leveraging existing resources from the Wisconsin Alzheimer's Disease Research Center and Wisconsin Registry for Alzheimer's Prevention. We compared two easily‐decentralized and low‐burden options for measuring preclinical changes–either alone or combined, testing associations of baseline plasma Aβ42/40 and intra‐individual cognitive variability (IICV) with longitudinal cognition. Methods: Using AA‐FAIM participants' cognitive data, we derived an estimate of baseline IICV from z‐scores, representing standard deviation across five indices from list learning, executive function, and confrontational naming tests. PrecivityADTM assays quantified baseline plasma Aβ42/40. After comparing baseline IICV associations for factor‐validity (Figure 1), we conducted linear mixed‐effects models examining associations of baseline plasma Aβ42/40, IICV, or the combination of markers with longitudinal cognition on eight outcomes. Fully adjusted models included age‐at‐assessment, gender, education, and random intercepts. Benjamini‐Hochberg (BH) corrections controlling false discovery rate at 5% were applied across outcomes. Using Bayesian Information Criterion (BIC), plasma Aβ42/40 models were assessed for comparative fits with and without IICV interactions and main effects. Results: Models included between 163‐667 observations from 56‐174 participants (Table 1). In models without plasma, IICV*age was weakly significant only for Trails tests with higher baseline IICV being associated with worse trajectories (Table 2; uncorrected/corrected p‐values=0.057/0.227 (log Trails A) and 0.044/0.227 (log Trails B)). Plasma Aβ42/40 was significantly associated with trajectories across multiple cognitive outcomes regardless of IICV inclusion (Table 3). Model estimates suggested lower baseline Aβ42/40 was associated with worse performance over time (Figures 2‐3). All interactions survived correction with IICV in the model. Without IICV, only Trails B survived correction. BIC comparisons suggested removing the IICV*age interaction from plasma models, but retaining IICV main effects. Conclusions: Baseline plasma Aβ42/40 predicted cognitive trajectory in a Black cohort. Although IICV appeared less sensitive than plasma Aβ42/40 in moderating cognitive trajectory, models including both IICV and plasma Aβ42/40 may be better in predicting trajectories. [ABSTRACT FROM AUTHOR]

Background: Cerebrospinal fluid (CSF) concentration of soluble TREM2 (sTREM2), a potential biomarker for microglial activation, is associated with attenuated longitudinal neurodegeneration and cognitive decline in Alzheimer's disease (AD), but data in early disease are lacking. This study's purpose was to use longitudinal volumetric imaging to assess the association of sTREM2 with age‐ and preclinical AD‐related grey matter (GM) changes. Method: Cognitively unimpaired participants (N = 384; amyloid‐positive N = 82) from the Wisconsin Registry for Alzheimer's Prevention and Wisconsin ADRC clinical core studies with baseline CSF biomarker and subsequent longitudinal T1‐weighted magnetic resonance imaging data were analyzed. CSF sTREM2 and phosphorylated‐tau181/amyloid‐beta1‐42 ratio (pTau/Aβ42) were measured using the NeuroToolKit panel of robust prototype assays (Roche Diagnostics International Ltd, Rotkreuz, Switzerland). T1‐weighted images were longitudinally registered to intra‐subject templates and segmented to create 58 GM regions of interest (ROIs) via the CAT12 longitudinal segmentation pipeline. Linear mixed‐effects models (random participant intercepts and age slopes) testing a three‐way interaction between time‐varying age, sTREM2, and pTau/Aβ42 to predict regional grey matter changes with all potential two‐way interactions and simple effects (adjusted for gender, years of education, intracranial volume, and head coil) were tested. In the event of a non‐significant three‐way interaction, the three‐way interaction was dropped and the model was reinterpreted. Statistical significance was considered at p <.05, uncorrected for multiple comparisons. Result: Age had a negative effect on regional GM volume across the brain and showed widespread interactions with pTau/Aβ42, indicative of accelerated decline with AD pathology. Negative three‐way interactions between sTREM2, pTau/Aβ42, and age were evident in the angular, supramarginal, lingual, and middle occipital gyri, predicting accelerated AD‐related longitudinal neurodegeneration with higher sTREM2 concentration. Negative two‐way interactions between pTau/Aβ42 and sTREM2 were evident in the supplementary motor cortex and superior frontal gyrus, indicating worse effects of AD‐pathology with higher sTREM2, regardless of age. Conclusion: Overall, higher sTREM2 may be associated with accelerated AD‐related neurodegeneration over time in the context of preclinical AD, particularly in posterior ROIs. Higher sTREM2 and underlying microglial activation may denote individuals at higher risk of experiencing the deleterious effects of early AD pathology on the brain. [ABSTRACT FROM AUTHOR]

Background: Apolipoprotein E4 (APOE‐ε4) is the strongest known risk factor for late‐onset Alzheimer's disease (LOAD). Variation in cognitive decline patterns among cognitively unimpaired individuals suggests that additional LOAD‐related genetic factors may interact with APOE‐ε4 to influence cognitive decline during asymptomatic aging. In the present study, we aim to investigate whether any genome‐wide association studies (GWAS) and p‐value threshold (pT)‐informed non‐APOE polygenic risk scores (PRS) moderate associations between APOE‐ε4 and longitudinal cognition. Methods: We leveraged 12 years of longitudinal data from the Wisconsin Registry for Alzheimer's Prevention, and we built eight PRSs under various pTs for single nucleotide polymorphism selection using summary statistics from three GWAS meta‐analyses: International Genomics of Alzheimer's Project (IGAP) case‐control study, UK‐biobank (UKBB) AD‐by‐proxy study, and a combined sample that merges IGAP and UKBB study results. All association analyses were fitted using a linear mixed effects model and adjusted for within‐individual/family correlation among 1,190 cognitively unimpaired individuals at baseline (Table 1). A set of sensitivity analyses were performed to assess the robustness of the main findings. Results: We found that the adverse effect of non‐APOE PRS on overall and memory‐related cognitive domains is more evident among APOE‐ε4 carriers as people age (Figure 1). There is a gradual, statistically significant difference in the rate of overall and memory‐related cognitive decline per unit change of non‐APOE PRSs between APOE‐ε4 carriers and non‐carriers for people over the age of 70 (Figure 2). Our study findings are consistent with a relatively stringent pT (p <= 5e‐8) and more evident when the PRSs are constructed using the case‐control GWAS study results, despite the smaller sample size ("Kunkle significant variants only" in Figures 1 and 2). Our findings are also robust to various sensitivity checks when we replace the dichotomous APOE‐ε4 predictor with the APOE genotype, use different PRS clumping parameters, and exclude cognitive measurements from the last test occasion, which has a smaller sample size. Conclusion: We recommend considering the interaction between non‐APOE PRS, APOE status, and age in modeling pre‐clinical cognitive phenotypes for LOAD. We also recommend leveraging a conservative p‐value threshold and using case‐control GWAS results in constructing the LOAD PRS. [ABSTRACT FROM AUTHOR]

Background: Cognitive reserve (CR) refers to the use of compensatory strategies related to executive functions (EFs) to reduce the negative effects of age‐related brain changes. Depression represents a risk factor for dementia, but little is known about its role in the association between CR and EFs. In the present study, we tested whether depression moderated the association between CR and EFs, and which variables among CR, depression and APOE‐e4 status (e4/non‐e4) predicted conversion to Cognitively Unimpaired‐Declining (CUD; Johnson et al., 2018) and Mild Cognitive Impairment (MCI) from a Cognitively Unimpaired‐Stable (CUS) baseline. Method: 416 participants were selected from the Wisconsin Registry for Alzheimer's Prevention. These were native English speakers, aged ≥50, CUS at baseline, and with no history of neurological or psychiatric disorders. Of these, 290 were still CUS, 97 were CUD and 29 had MCI at the 10‐year follow‐up visit. Depression was assessed with the 20‐item Center for Epidemiologic Studies Depression Scale (CES‐D). A standardized composite score from three tests (CFL, Digit Span, Letter‐Number sequencing) estimated EFs. Another composite score, based on the WRAT‐3 Reading subtest (a measure of literacy or premorbid abilities) and years of education estimated CR. A hierarchical linear regression was performed to evaluate the effect of CR and depression on EFs at follow‐up. Baseline demographic variables (age, gender, APOE‐e4) and diagnosis at follow‐up (CUS, CUD, MCI) were entered in Model 1, CR and depression in Model 2, and the CR*Depression interaction in Model 3. Moreover, a multinomial logistic regression was used to predict conversion to CUD and MCI from baseline. Result: Model 3 added significantly onto Model 2 (p =.024), indicating a moderation between CR and depression on EFs. Particularly, the beneficial effects of CR on EFs seem to be mitigated by increasing levels of depression. In the multinomial regression, depression predicted conversion to CUD from CUS while CR, depression and APOE‐e4 predicted conversion to MCI from CUS. Conclusion: These findings support the hypothesis that CR, associated with EFs, may protect against cognitive aging, but also highlight the disruptive effect that depression has on long‐term cognitive health. Further studies may replicate these findings in clinical populations. [ABSTRACT FROM AUTHOR]

Background: Gut bacterial metabolites in circulation and postmortem brain tissue differ in abundance between cognitively unimpaired (CU) and Alzheimer's disease (AD) individuals. Furthermore, in mice chronic injection of bacterial components exacerbates AD pathology. In CSF, we found the microbially‐produced trimethylamine‐N‐oxide was linked to AD and neurodegeneration biomarkers. However, the broader extent to which microbe‐modulated metabolites relate to AD and neurodegeneration is unknown. We tested the hypothesis that in CSF of non‐demented adults, elevated metabolites would longitudinally associate with higher biomarkers. Method: CSF was collected biannually in the Wisconsin Alzheimer's Disease Research Center and Wisconsin Registry for Alzheimer's Prevention studies. Biomarkers of pTau181, Aβ42, tTau, axonal (NfL) and synaptic (neurogranin, α‐synuclein) degeneration were quantified using Elecsys assays (NeuroToolKit robust prototype, Roche Diagnostics). Metabolite relative abundances were quantified via untargeted metabolomics analysis (ultrahigh performance liquid chromatography‐tandem mass spectrometry, Metabolon). A priori literature and database review identified 76 detected microbe‐modulated metabolites. 200 CU participants with two or more analyzed CSF samples were included in statistical analysis (Table 1). For each metabolite‐biomarker pair, multilevel linear growth modeling tested person‐level mean metabolite effects on biomarker slope and intercept, and time‐varying metabolite effects on biomarker intercept (Figure 1). Covariates included age, sex, and APOE risk score; Benjamini‐Hochberg corrections adjusted for Type I error inflation. Result: Seven metabolites were positively associated with biomarker slope or intercept (Table 2). Mean gluconate, glycerate, and oxalate levels were correlated with increased tTau slope, and N‐acetylaspartate, N‐acetylglutamate and cholesterol were correlated with higher a‐synuclein or neurogranin intercepts. Phenol sulfate was linked to tTau and α‐synuclein trajectories. To our knowledge, this is the first study detecting phenol sulfate in human CSF. No metabolites significantly associated with NfL or pTau181/Aβ42. Conclusion: In CSF, participants with higher mean levels of bacteria‐utilized metabolites exhibited accelerated neurodegeneration (tTau). Gluconate is metabolized by Bifidobacterium and Lactobacillus, two genera reduced in AD. Furthermore, the microbially‐produced phenol sulfate predicted greater tTau development and synaptic degeneration. Uremic toxins related to phenol sulfate dysregulate endothelial barriers in the central nervous system and vascular tissues. Additional studies should evaluate microbial metabolites as mediators of gut microbiome‐neurodegeneration relationships, and should determine if they represent modifiable pathways. [ABSTRACT FROM AUTHOR]

Background: Sex differences in tau deposition are apparent in clinically normal older adults. Abnormal β‐amyloid (Aβ) may strengthen this association between sex and tau. Whether hormone therapy (HT) and menopause influence this higher tau retention in women and whether this is contingent upon levels of Aβ burden is unclear. We examined differences in the cross‐sectional association between Aβ and regional tau deposition as a function of sex, HT‐exposure and age‐at‐menopause. Method: Data were collected from 292 cognitively unimpaired individuals from the Wisconsin Registry for Alzheimer's Disease (mean age 67.43; 193 women [66%]; 106 APOE ε4 carriers [37%]) who underwent dynamic0‐70 [C‐11] Pittsburgh Compound‐B [PiB] and [F‐18]‐MK‐6240 PET. The PiB distribution volume ratios (DVR) index reflects cortical Aβ burden. 90 [46.6%] women reported use of HT (past/current). Seven a priori tau regions previously demonstrating sex differences were selected (entorhinal cortex, amygdala, inferior temporal gyrus, middle and inferior temporooccipital gyri, superior parietal lobule and fusiform gyrus). Linear regressions (covarying current age) examined the sex*PiB‐DVR‐index interaction for each tau‐PET outcome and a preclinical Alzheimer's cognitive composite (PACC). Similar models were examined in women alone for the same outcomes, investigating HT*PiB‐DVR‐index and age‐at‐menopause*PiB‐DVR‐index interactions (modeled separately). Result: Women exhibited higher tau‐PET with an increasing PiB‐DVR‐index relative to men (FDR p's: <0.002 in 5 of 7 tau regions; Fig 1). The HT*PiB‐DVR‐index analyses indicated that women with a higher PiB‐DVR‐index and self‐reported HT use exhibited higher tau in five regions (Fig2). Exploratory analyses suggested the HT*PiB‐DVR‐index interaction was strongest in women over the median current age (63.5years). The age‐at‐menopause* PiB‐DVR‐index interaction was significant in all regions and for the PACC, revealing subtle cognitive deficits in women with earlier age‐at‐menopause.and a higher PiB‐DVR‐index burden (Fig3). Conclusion: Replicating previous studies, tau deposition was elevated in women compared with men. Hormone therapy use and earlier age‐at‐menopause influenced the association between Aβ and tau and between Aβ and cognition. Cognitively unimpaired women with elevated Aβ burden may be vulnerable to hormonal changes related to hormone therapy and early menopause and should be prioritized for future anti‐tauopathy clinical trials. [ABSTRACT FROM AUTHOR]

Background: The prevalence of clinical Alzheimer's disease (AD) and type 2 diabetes is higher in African Americans (AA) relative to non‐Hispanic White Americans. Although diabetes is a risk factor for Alzheimer's clinical syndrome, it is unclear whether diabetes associates with amyloid‐β, a pathological hallmark of AD. We investigated whether diabetic status was associated with age‐related decline in plasma amyloid‐β (Aβ) 42/40 ratio and cognition in a predominantly dementia‐free AA cohort. Method: Participants (N=314) were middle‐aged and older adults (Table 1) enrolled in the African Americans Fighting Alzheimer's in Midlife cohort. EDTA plasma was collected at 1‐8 visits for Aβ 42/40 quantification using immunoprecipitation mass spectrometry (C2N Diagnostics, St. Louis, MO). 52% had >1 plasma Aβ 42/40 measurements over an average interval of 4.9 years (SD=3). Recall (immediate and delayed) and executive functioning were assessed using Rey's Auditory Verbal Learning Test and Trails B, respectively. Diabetic status was determined from clinician‐report, self‐report, self‐report of antidiabetic medication use or fasting glucose ≥126 mg/dL. Cognitive data from the first of 1‐6 visits and diabetic status were obtained at the same visit as baseline plasma Aβ 42/40 for 96% of participants. Linear mixed‐effects models tested diabetic status as a moderator of age‐related decline in plasma Aβ 42/40 ratio and cognition. If age x diabetic status was not significant, diabetic status was tested without the interaction. Covariates included APOE4 carrier status and sex, and education was included when cognition was the outcome. Results: Diabetic status did not significantly moderate age‐related decline in plasma Aβ 42/40 (β=0.0001, p=.21) or predict average plasma Aβ 42/40 ratio (β=‐0.001, p=.22). Diabetes was also not significantly related to aging‐associated decline in cognition but was a significant predictor of worse average Trails B scores (Table 2). Results from a cognitively unimpaired subsample (n=261) revealed a similar pattern. Conclusion: In a predominantly dementia‐free AA cohort, diabetes was unrelated to plasma Aβ 42/40 but was related to worse Trails B performance. Poor Trails B performance has been linked in previous studies to cerebrovascular dysfunction. Determining mechanisms, vascular and AD pathological pathways, that link diabetes to increased dementia risk in AAs warrants further investigation. [ABSTRACT FROM AUTHOR]

Background: Studies of anticholinergic (AC) medications (drugs that block acetylcholine neurotransmitter action) have shown associations between the AC medications use, cognitive impairment, and increased dementia risk. Associations between AC medication and AD biomarkers among cognitively normal late middle‐aged adults are unclear. This study examined associations between AC use, cognition, physical health, and amyloid PET measures. Methods: Wisconsin Registry for Alzheimer's Prevention (WRAP) participants with >=3 cognitive composite scores, free of dementia at baseline cognition (n=1262) were included in analyses of cognitive and general health measures; PET amyloid measures were examined in a subset with Pittsburgh Compound B PET imaging (PiB; n=299). Participants were classified as either taking or not taking at least one AC medication at baseline (AC+, n=437; AC‐, n=825; Table 1). Cross‐sectional analysis of baseline associations examined AC+/AC‐ differences in AD‐related cognitive composites (e.g., memory, PACC) and health markers (e.g., depression, comorbidities). Linear mixed‐effects models (LME) examined whether AC modified age‐related cognitive trajectories in APOE ε4 carriers or non‐carriers and PiB positive or negative group. Sensitivity analyses repeated models only in the high AC+ (AC score > 1) group (n=191) relative to the AC‐ group. The statistical threshold for significance was set at P <.05. Results: Mean(sd) age for AC+ and AC‐ participants was 55.4(6.4) and 53.7(6.6). The AC+ group was significantly higher than the AC‐ group on depression scores, BMI, total medications, total comorbidities, and cardiovascular problems (Table 2). The AC groups did not differ on amyloid PET measures (Table 3). At baseline cognition, the AC+ group (n=437) showed lower executive function and PACC3 scores (Figure 1). Longitudinal analyses indicated that AC+ predicted a steeper decline slope in executive function and PACC3 with effects exacerbated in individuals with AD risk factors (Fig. 2‐3, respectively). The slopes of AC + group differed significantly after age 65. Conclusion: AC medication use was associated with faster cognitive decline, particularly among APOE4 carriers and those with PET‐based evidence of amyloid plaques, suggesting AC use may exacerbate AD‐related cognitive decline in the preclinical time frame. The results have implications for AC usage in older adults with AD risk or biomarker positivity. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) diagnostics is evolving rapidly, moving toward biomarker‐based diagnoses characterized by amyloid, tau, and neurodegeneration. To clarify benefits and risks of using this framework with cognitively unimpaired adults, particularly those from groups under‐represented in AD biomarker research, we developed a 30‐minute telephone survey to identify factors predicting willingness to enroll in biomarker studies and hypothetical disclosure‐related outcomes. Method: Existing scales were modified for telephone administration to assess experience/concern about AD, perceived health, health locus of control, brain health social norms, trust in medical research, and experiences of discrimination. Five hypothetical scenarios assessed willingness to participate in biomarker studies with or without results disclosure. Three open‐ended items provided qualitative data on willingness to participate and share results with others. Forty‐one items assessed reasons for learning results and potential reactions following a hypothetical elevated result. Descriptive statistics were used to evaluate response patterns. Thematic analysis of open‐ended items was conducted using an inductive coding method. Result: Cognitively unimpaired participants (N=334) included 148 Black/African Americans and 186 non‐Hispanic Whites; aged 45‐89; Table 1). Most participants had cared for someone with dementia (73%), desired to know if they will develop AD (82%) and trust medical research (mean RAQ‐7 = 30/35) (Table 2). Approximately 50% were very or extremely willing to participate in biomarker studies regardless of results disclosure; however, African Americans were less willing to participate if results were not disclosed. Common themes related to willingness to participate and learn results included wanting to know if at personal risk or to prevent/plan for AD and wanting to support science or help others. Misgivings/anxiety about knowing biomarker and concerns about invasiveness or lack of a cure for AD were common themes for those less willing (Table 3). Conclusion: This survey assesses AD‐relevant, health, and experiential factors, willingness to participate in biomarker research, and disclosure‐related outcomes. Initial results suggest understanding risk, prevention/planning behaviors, and helping others are reasons for participating and learning biomarker results. Results may also inform recruitment of African Americans into biomarker studies. Future analyses will investigate factors predicting potential positive (e.g., planning, risk‐reducing behavior) and negative (psychological distress/stigma) disclosure outcomes. [ABSTRACT FROM AUTHOR]

Background: With improved temporal identification and characterization of AD biomarkers, disclosure of personal biomarker information will become increasingly relevant. Understanding participant factors related to willingness to learn such information may inform relationship‐building with communities, especially those historically excluded from biomedical research, and aid the development of guidelines for safe, effective disclosure. Method: We developed a 30‐minute phone survey to gather information from Black and W participants on willingness to enroll in different types of biomarker disclosure studies (general AD biomarker, PET, CSF, and blood‐based), reasons for enrolling, and potential outcomes following a hypothetical positive result. Data were collected from cognitively healthy participants (n=334, mean age=64.8±7.7, 45% Black) enrolled in the Wisconsin ADRC or Wisconsin Registry for Alzheimer's Prevention. Using linear regression, we assessed the relationship between willingness to enroll in various hypothetical AD biomarker disclosure studies and age, sex, race, education, family dementia history, trust in medical research, concern about developing AD, and experiences of discrimination. Result: Trust in medical research and race were significant predictors of willingness to enroll in all the hypothetical AD biomarker disclosure studies, with higher willingness associated with greater trust in research and with White racial identification. We did not find a significant difference in trust in medical research between Black and White participants (t(df)=‐1.42(299.13), p.14). In secondary analyses, we included covariate interactions with race to assess racial differences in their effects. We found that the gender gap for Black participants in willingness to enroll in an AD CSF disclosure study was larger than that for White participants, but other effects were nonsignificant. Conclusion: These results suggest that trust in medical research is related to willingness to enroll in AD biomarker studies that disclose results to participants. As race is a social construct representing a combination of ancestry and experience, we plan to further explore the relationship between study enrollment willingness and race through a mixed‐methods approach examining quantitative and qualitative data. Understanding factors related to participant enrollment willingness may be crucial for developing culturally informed best practices for personal biomarker disclosure and in engaging and bolstering representation from historically minoritized groups into AD biomarker research. [ABSTRACT FROM AUTHOR]

Background: Previous studies suggest associations between self‐reported poor sleep and relevant health markers and AD‐related outcomes including cognition and AD pathology. It is important to understand how sleep, a modifiable risk factor, may be related to cognition and other measures of aging in late midlife when AD pathology may be accruing. The study objectives included examining whether sleep profiles were associated with concurrent health and cognition, and positron emission tomography (PET) measures of amyloid. Methods: Dementia‐free participants from the Wisconsin Registry for Alzheimer's Prevention (WRAP) who had completed sleep questionnaires were eligible (n=619); questionnaires included the Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), and Medical Outcomes Study Sleep Scale (MOS). K‐means clustering analysis identified three clusters. We compared concurrent health markers (e.g., depression and insulin resistance) and cognitive composite scores across the sleep profile clusters. In the subset with Pittsburgh Compound B (PiB; n=107) PET imaging, we also compared amyloid measures (estimated global PiB distribution volume ratio (DVR), PiB chronicity, and PiB(+) (DVR>=1.2) at sleep assessment. Significant omnibus tests (p<.05) were followed with pairwise comparisons. Results: Mean(sd) sample baseline age was 62.6(6.7). Cluster analysis identified 3 groups: Healthy Sleepers (HS, n=262(42.3%)), Intermediate Sleepers (IS, n=229(37.0%)), and Poor Sleepers (PS, n=128(20.7%); Fig. 1, Fig. 2, Table 1). All omnibus tests comparing demographics and health measures across sleep groups were significant except age, gender, and APOE e4 carriers; the PS group was worse than HS and/or IS on all other measures, including self‐reported depression, health, and memory complaints (Table 2). PS performed worse than HS and/or IS on measured body mass index, waist‐hip ratio and insulin resistance (Fig. 3a); and after adjusting for covariates, PS performed worse on all cognitive outcomes except working memory (Fig. 4). There were no differences between sleep groups on measures of amyloid (Fig. 3b). Conclusion: Sleep profiles were associated with differences in additional modifiable risk factors and concurrent cognition, but not amyloid. Further analysis was needed in the association between the sleep constructs and amyloid clearance/accumulation. Management of sleep and related factors may provide an opportunity for early intervention that could ultimately delay or prevent clinical impairment. [ABSTRACT FROM AUTHOR]

Background: New methods have recently been developed to model longitudinal amyloid biomarkers in AD. This work compares three methods in three different cohorts, and investigates associations of APOE‐e4 status with amyloid onset age and amyloid accumulation rates. Method: Participants from ADNI (N=1213), BLSA (N=205) and WRAP (N=272) with available amyloid PET imaging and APOE genotype were included in the study (Table 1). Amyloid (A) burden was quantified using established methods (ADNI: BAI processed SUVR, WM reference region; BLSA: SRTM‐LRSC DVR, PVC, cerebellum GM reference region; WRAP: graphical analysis DVR, cerebellum GM reference region). Group‐based trajectory modeling (GBTM), ordinary differential equation‐Gaussian process (ODE‐GP) and sampled iterative local linear approximation (SILLA) methods were used to model longitudinal amyloid accumulation, estimate A+ onset age, and align amyloid values by years A+ for each cohort. Forward and backward SUVR/DVR prediction, estimated age of A+ onset, and associations of DVR/SUVR residuals with age, time from reference scan, and SUVR/DVR, were compared for each model and cohort. The impact of APOE genotype on A+ onset and amyloid PET accumulation trajectories was investigated using discrete rate sample, Kaplan‐Meier curves, and accelerated failure time models. Result: All methods produced similar amyloid vs. years A+ curves for each cohort (Figure 1) with most notable discrepancies occurring in the A‐ range. DVR/SUVR prediction performance was similar across methods and was better for retrospective vs. prospective prediction. Amyloid accumulation rates and trajectories were similar for APOE‐e4+ and APOE‐e4‐ genotypes (Figure 2). Kaplan‐Meier curves were different between the APOE groups (log‐rank test, p<0.005 for each test; Figure 3), with the e4+ group exhibiting earlier A+ onset ages compared to e4‐ (Table 2) for all methods and cohorts. Conclusion: This work demonstrates similar performance of three novel methods for modeling longitudinal amyloid PET burden and estimating individualized A+ onset age. Results suggest that APOE‐e4 status affects the A+ onset age, but not the rate of amyloid PET accumulation. Ongoing work is investigating potential differences in amyloid accumulation trajectories and A+ onset age between APOE genotypes (e.g. e3e3, e3e4, e4e4,...) and is investigating relationships between A+ onset, APOE genotype and onset of cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: Asymmetric binding patterns in tau PET imaging are sometimes observed, but less is known about how this asymmetry might explain clinical phenotypes and AD variants, especially in a longitudinal context. Previous work has demonstrated that unilateral tau deposition in left frontal and temporal regions is common in logopenic progressive aphasia (lvPPA), an atypical AD variant. This work presents two descriptive case studies of participants with opposing laterality of [18F]MK‐6240, a PET radioligand for AD neurofibrillary tangles. Method: Two participants from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), an ongoing, longitudinal cohort study of late middle‐aged adults enriched for AD risk, were identified based on unilateral MK‐6240 deposition. Topographic amyloid and tau burden was assessed with [11C]PiB and [18F]MK‐6240 PET imaging, respectively. Demographic data, cognitive diagnoses, health histories, and APOE status are described. Longitudinal neuropsychological assessments and digitally recorded connected speech samples were analyzed to create a descriptive profile of cognition and connected speech‐language (CSL). Cognitive test scores and language measures were then compared against the scores of the full WRAP sample, using a robust normative approach previously described by our group. Result: MK‐6240 tau‐PET imaging revealed right‐lateralized tau in Participant A and left‐lateralized tau in Participant B (Figure 1). Participants were identical on all demographics except age and APOE risk of developing AD (though both participants were ApoE‐4 allele carriers) (Table 1). Cognitive assessment Z‐scores (0=mean of demographically similar cognitively stable WRAP participants; Figs 2‐4) depict considerable between visit variability for Participant A on all measures, including language and CSL, with decline from baseline on all but letter fluency. Participant B showed isolated baseline deficits on memory tests (AVLT and story recall) at two standard deviations below the mean, which declined further across visits, whereas performances on Trails, naming, category fluency, and all measures of CSL remained stable and within normal expectations. Conclusion: In this case study, right>left tau PET signal was associated with greater decline in language, particularly connected speech and naming. Future work will examine associations between volumetric MRI and asymmetric regional tau deposition, as well as additional relationships to language and cognition. [ABSTRACT FROM AUTHOR]

Background: Subtle changes in connected speech language (CSL) comprising fluency disruptions and limited semantic content are present in preclinical stages of Alzheimer's disease (AD) and dementia. Our prior research suggests that individuals identified by PET imaging as amyloid‐positive experience more rapid decline of semantic content than those without amyloid. Here we expand our analyses to additional CSL measures sensitive to nascent deficits in communication. Method: Study participants were enrolled in Wisconsin Alzheimer's Disease Research Center (n= 103) or the Wisconsin Registry for Alzheimer's Prevention (n= 223), had available cerebrospinal fluid (CSF) measurements, and 1‐4 CSL samples. CSF biomarkers were measured using the exploratory Roche NeuroToolKit assays, a panel of automated robust prototype immunoassays (Roche Diagnostics International Ltd). Speech samples (N=740) were recorded while describing the "Cookie Theft" picture from the Boston Diagnostic Aphasia Examination. The majority of participants (65%) provided ≥ 2 language samples across biennial visits. Moving average types‐tokens ratio (MATTR), content information unit (CIU) density, CIUs per minute, words per minute, and filled pauses were extracted from speech samples. Linear mixed effects models were used to test moderation of age related changes CSL samples by amyloid status (Aβ42/Aβ40 ≥.046), where age at study visit is the measure of time. ovariates gender, years of education, and number of CSL visits (i.e., practice effects) were included as fixed effects. Result: Baseline demographics and study variables stratified by amyloid status are shown in Table 1. CSF amyloid status did not moderate longitudinal changes in any of the CSL outcomes (Figure 1). After dropping these non‐significant interactions, amyloid‐positive individuals expressed fewer CIUs total and per minute and slightly more filled pauses (Table 2). We also found a significant decline with age in CSL outcomes MATTR, CIUs per minute, and words per minutes. The pattern of results was unchanged after excluding nineteen individuals with cognitive impairment from analyses. Conclusion: We found that amyloid burden associated with connected speech measures reflecting semantic content and word retrieval. CSL is a 'digital biomarker' that shows promise as a measure of language; future work will continue to explore longitudinal relationships between CSL and AD biomarkers. [ABSTRACT FROM AUTHOR]

Background: Quantifying the temporal progression of key pathophysiological events in Alzheimer's disease is crucial for staging disease, dementia risk assessment, and informing clinical trials. This work extends a novel algorithm (sampled iterative local linear approximation; SILLA) developed for modeling PET trajectories to: 1) generate a unified CSF and PET amyloid accumulation timeline, and 2) characterize the temporal progression of biomarker and cognitive changes in AD. Method: 941 participants from the Wisconsin Registry of Alzheimer's Prevention, Wisconsin Alzheimer's Disease Research Center, and Statins in Healthy, At‐Risk Adults: Impact on Amyloid and Regional Perfusion studies underwent [C‐11]PiB PET and/or lumbar puncture; and a subset underwent [F‐18]MK‐6240 PET and anatomical MRI using standardized protocols (Table 1). Amyloid burden was quantified from cortical PiB DVR and CSF Aβ42/40. Amyloid Time, quantified as years from CSF Aβ42/40 positivity, was estimated by simultaneously modeling Aβ42/40 and PiB DVR using an extension of the SILLA method in a subset of 248 participants with both measurements. CSF biomarkers measured using the exploratory Roche NeuroToolKit assays, imaging biomarkers, and cognitive impairment (global CDR, CDR SOB, and diagnosis) were plotted as a function of Amyloid Time. Time between key AD events was estimated using LOWESS curves and positivity thresholds for each AD biomarker and CDR scores. Result: Figure 1 depicts biomarker thresholds. Amyloid Time suggested PET Aβ+, CSF T+, PET T+, CDR=0.5, and CDR=1 occurred 4.0, 12.0, 13.8, 17.7, and 22.0 years following Aβ42/40 positivity (Figure 2, Table 2). Heterogeneity was observed in tau levels relative to Amyloid Time for individual cases. Those with worsening CDR generally had high amyloid and at least one T+ biomarker. Conclusion: Amyloid Time based on combined CSF and PET amyloid biomarkers and the SILLA algorithm provides a novel way to quantify the duration of amyloid exposure from the earliest pathophysiologic CSF changes to dementia onset in AD within individuals. This enables characterization of the time between key AD events, which could inform prognosis, identify intervention timing for novel therapies, and enable exploration of mechanisms related to resistance and/or resilience. [ABSTRACT FROM AUTHOR]

Background: This work examined heterogeneity in cognitive trajectories between amyloid(A) and tau(T) cerebrospinal fluid (CSF) stratified biomarker groups in late‐middle‐aged, initially cognitively unimpaired individuals. Additionally, candidate biomarkers of neurodegeneration were investigated for potential added value in predicting cognitive decline. Method: Individuals (N=535) recruited from Wisconsin Alzheimer's Disease Research Center (n=301) and Wisconsin Registry for Alzheimer's Prevention (n=234) underwent a lumbar puncture and serial (1 to 9) cognitive assessments (Table 1). CSF biomarkers were measured using the exploratory Roche NeuroToolKit assays (Roche Diagnostics International Ltd). Four biomarker groups (A‐T‐, A‐T+, A+T‐ and A+T+) were established based on combinations of CSF Aβ42/40 and p‐tau biomarker positivity, where (A) and (T) positivity was determined as CSF Aβ42/40≤0.046 and CSF p‐tau≥24.8 pg/mL. Decline on a Preclinical Alzheimer's Cognitive Composite (PACC‐3) was investigated using linear mixed effects models (including subject‐specific random intercepts and age‐related slopes) adjusting for sex, education, cohort, and the number of prior exposures to the cognitive battery. In primary analyses, the predictors of interest included the biomarker group and its interaction with age to investigate whether longitudinal PACC‐3 trajectories differed between biomarker groups. Secondary models added one of the following biomarkers for neurodegeneration and its interaction with age: neurofilament light chain (NfL), neurogranin, or alpha‐synuclein. Models were compared against the primary model using the Akaike information criterion and log likelihood ratio tests. Result: The results from the primary model indicated a significant biomarker group×time interaction (P<.001, Figure 2). Tukey‐adjusted pairwise comparisons indicated that the A+ groups (A+T‐ and A+T+) had significantly different PACC‐3 trajectories compared with the A‐T‐ group (P<.0001, Figure 2). Comparison of the simple age‐related slopes suggest that the A+T+ group was declining approximately 3.6 times faster compared with those without elevated biomarkers. Higher NfL, but not neurogranin or alpha‐synuclein, was associated with PACC‐3 decline after accounting for AT(+/‐) status (Table 2). Conclusion: In this preclinical sample, cognitive decline was evident in A+ groups. Secondary analyses suggest that NfL may contribute additional value to the AT(neurodegeneration) framework in explaining cognitive decline before the onset of dementia. [ABSTRACT FROM AUTHOR]

Background: Several Alzheimer's disease (AD) metabolomics studies in cerebrospinal fluid (CSF) and plasma identified associations between sphingomyelin metabolites (SMs) and AD. However, the results were inconsistent, potentially due to small sample sizes. They were limited in the AD, neurodegeneration, and neuroinflammation biomarkers available for additional analyses. Leveraging two longitudinal preclinical and clinical AD cohorts with CSF metabolomics data, we analyzed the relationship between CSF SMs and biomarkers and measures of AD (PET Pittsburgh Compound B [PiB], amyloid‐β42/40, phosphorylated‐tau181, phosphorylated‐tau181/amyloid‐β42), neurodegeneration (neurofilament light [NFL], neurogranin, α‐synuclein), and neuroinflammation (soluble triggering receptor expressed on myeloid cells 2 [sTREM2], interleukin‐6 [IL‐6], chitinase‐3‐like protein 1 [YKL‐40]). Method: We used data from the Wisconsin Registry for Alzheimer's Prevention (WRAP) and the Wisconsin Alzheimer's Disease Research Center (ADRC). Participants were aged 45‐91 years with available CSF metabolomics and biomarker data (n = 493 individuals [726 visits]; 44 individuals had diagnosed AD, 40 had mild cognitive impairment [MCI], and 409 were cognitively unimpaired). CSF biomarkers were measured with the Roche NeuroToolKit assays, a panel of automated robust prototype immunoassays. Associations between 12 SMs and MCI and AD diagnoses were analyzed using logistic regressions. Cross‐sectional global amyloid burden in the brain, measured by PiB‐PET, was regressed on the SMs using linear regression models. The nine other longitudinal CSF biomarkers for AD, neurodegeneration, and neuroinflammation were regressed on SMs using linear mixed models. All models were adjusted for sex and age. A Bonferroni correction (p<3.47×10‐4) was used to determine statistical significance. Result: No SMs were significantly associated with MCI or AD diagnosis (Table 1) or amyloid markers (Table 2). Phosphorylated‐tau181 was significantly, positively associated with six SMs. NFL and neurogranin were significantly, positively associated with 11 SMs. α‐synuclein was significantly, positively associated with 6 SMs (Table 3). sTREM2 and YKL40 were significantly, positively associated with 9 and 5 SMs, respectively (Table 4). Stearoyl SM had the strongest association for each set of SM‐biomarker regressions. Conclusion: Associations between SMs and biomarkers of neurodegeneration and neuroinflammation, but not markers of amyloid or diagnoses of MCI or AD implicate SMs as potential biomarkers for neurodegeneration and neuroinflammation that may not be AD‐specific. [ABSTRACT FROM AUTHOR]

Background: The cerebrospinal fluid (CSF) amyloid β‐negative and tau‐positive (A‐T+) biomarker profile, as defined by normal amyloid β42/40 ratio (Aβ42/40) and increased phosphorylated tau (p‐tau), is sometimes seen in daily clinical practice. However, knowledge is lacking on its prevalence and whether patients with this profile are at risk for biological disease progression and cognitive decline. Method: In this study, data from four cohorts were included: UGOT (the clinical laboratory database at Sahlgrenska University Hospital, Gothenburg), Alzheimer's Disease Neuroimaging Initiative (ADNI; cognitively unimpaired [CU] and mild cognitive impairment [MCI] individuals), Wisconsin Registry for Alzheimer's Prevention, and Wisconsin ADRC cohorts (latter two analyzed jointly, "WISC"; predominantly CU individuals). Repeated measures of cognitive decline (modified preclinical Alzheimer's composite [mPACC] in ADNI, and PACC‐3 in the WISC sample), amyloid accumulation (only in ADNI: [18F]‐florbetapir amyloid positron emission tomography [PET]) neurodegeneration (only in ADNI: magnetic resonance imaging [MRI], [18F]‐fluorodeoxyglucose PET [FDG‐PET]), and cross‐sectional tau PET ([F18]‐flortaucipir in ADNI and [F18]‐MK6240 in WISC) were used as exposures. Result: In the UGOT cohort, a total of 7679 individuals were included (46 Conclusions: 6% women; mean [SD] age, 71.0 [8.3] years). Further, 970 individuals from ADNI (45.8% women, mean [SD] age, 72.8 [7.0]), and n = 519 individuals from WISC (66.7% women, mean [SD] age, 59.7 [7.3]) cohorts were included. The prevalence of the A‐T+ profile in the UGOT cohort was 4.1%, compared with A+T+ that constituted 36%, A‐T‐ 42%, and A+T‐ 17%. In ADNI and UGOT, A‐T+ had higher concentrations of Aβ42 and Aβ40 compared to A‐T‐ individuals. The prognosis of CSF biomarker profiles was determined with linear mixed effects models (ADNI and WISC). The A‐T+ profile, compared with A‐T‐, presented no statistically significant rates of cognitive worsening (ADNI and WISC p>0.05), or in longitudinal changes in Aβ‐PET, FDG‐PET, and hippocampal atrophy (all p>0.05), within the ADNI subsample. Cross‐sectionally, A‐T+ individuals had similar tau‐PET uptake to A‐T‐ individuals (p>0.05). Conclusion: The CSF A‐T+ profile is to be considered clinically benign, as individuals with this profile do not have a higher rate of cognitive decline compared with biomarker‐negative individuals and do not display signs of amyloid accumulation or progressive neurodegeneration. [ABSTRACT FROM AUTHOR]

Background: The analysis of the longitudinal progression of plasma biomarkers and amyloid imaging might lead to new insights in the early pattern of biomarker changes. Method: Plasma Aβ42/Aβ40, pTau231/Aβ42, pTau181/Aβ42, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NFL) were measured for n = 223 subjects and 1673 total visits in the Wisconsin Registry for Alzheimer's Prevention (WRAP) using Quanterix Simoa assays at the University of Gothenburg. Global distribution volume ratio (DVR) was computed from 11C‐Pittsburgh compound B PET scans. Inclusion criteria included Amyloid or CSF Amyloid positive status, and at least one plasma serial value. Trajectories spanning age 46.9 to 80.3 years old for the DVR and plasma biomarkers were estimated by fitting a vector field to the longitudinal data (https://arxiv.org/abs/2206.15215). A Louvain community clustering analysis was performed. The time from Amyloid positivity (DVR = 1.19; centiloid of 22) was also computed for all individual trajectories that crossed this threshold. Result: The Louvain community clustering algorithm resulted in three distinct communities (Fig 1): no increase in DVR or plasma biomarkers (Green, n = 88(39.5%)); increase in DVR and all plasma biomarkers except NFL (Red, 41(18.4%)); and mixed DVR plus GFAP and NFL increase (Blue, 94(42.2%)). Time since Amyloid positivity (TSAP) was computed for the subset of subjects (n = 84) expected to cross the Amyloid positivity threshold. The Louvain communities as a function of TSAP are shown in Fig 2. Conclusion: We identified three communities regarding the longitudinal progression of amyloid PET and plasma biomarker levels. Plasma biomarkers were dynamic up to 30 years before Amyloid positivity. [ABSTRACT FROM AUTHOR]

Introduction: This study examines the utility of a multipanel of cerebrospinal fluid (CSF) biomarkers complementing Alzheimer's disease (AD) biomarkers in a clinical research sample. We compared biomarkers across groups defined by clinical diagnosis and pTau181/Aβ42 status (+/−) and explored their value in predicting cognition. Methods: CSF biomarkers amyloid beta (Aβ)42, pTau181, tTau, Aβ40, neurogranin, neurofilament light (NfL), α‐synuclein, glial fibrillary acidic protein (GFAP), chitinase‐3‐like protein 1 (YKL‐40), soluble triggering receptor expressed on myeloid cells 2 (sTREM2), S100 calcium binding protein B (S100B), and interleukin 6 (IL6), were measured with the NeuroToolKit (NTK) for 720 adults ages 40 to 93 years (mean age = 63.9 years, standard deviation [SD] = 9.0; 50 with dementia; 54 with mild cognitive impairment [MCI], 616 unimpaired). Results: Neurodegeneration and glial activation biomarkers were elevated in pTau181/Aβ42+ MCI/dementia participants relative to all pTau181/Aβ42‐ participants. Neurodegeneration biomarkers increased with clinical severity among pTau181/Aβ42+ participants and predicted worse cognitive performance. Glial activation biomarkers were unrelated to cognitive performance. Discussion: The NTK contains promising markers that improve the pathophysiological characterization of AD. Neurodegeneration biomarkers beyond tTau improved statistical prediction of cognition and disease stages. [ABSTRACT FROM AUTHOR]

Background: There is growing interest in using digital biomarkers, like recorded spontaneous speech, as indicators of Alzheimer's disease progression. Several studies have shown that changes in connected speech and language (CSL), including speech fluency markers and linguistic content, are associated with preclinical cognitive decline. Little is known about the relationships between CSL and the lifestyle factors that may be largely dependent upon it, particularly social participation. Social engagement (SE) (e.g., in activities such as informal social participation, volunteering) has been associated with multiple health indicators in aging. Our aim was to determine if SE at baseline, or change in SE, was related to declines in CSL measures that have been linked to cognitive decline. Sensitivity analyses determined if baseline CSL predicted baseline SE and change in SE. Method: Longitudinal CSL measures (Figure 1) were automatically extracted from picture description transcripts from Wisconsin Registry for Alzheimer's Prevention participants (N=794). SE composite scores were created from selected Florida Cognitive Activities Scale items (Table 1). Statistics included t‐tests, chi‐square tests, ANCOVA, and linear mixed‐effects models with CSL as outcomes and baseline SE, and normalized change from baseline to most recent SE, as predictors. Result: Table 2 shows speech metrics and demographics of participants stratified by low, medium and high SE. Participants reporting "high" SE were more likely to be female, had higher baseline WRAT‐3 reading scores, and had lower depression scores. Participants reporting "low" SE expressed fewer content words, unique words, and total words than those with mid‐ or high SE. Positive change in SE was associated with slower age‐related change in speech fluency over time (Table 3, Figure 2). Sensitivity analyses indicated worse baseline speech fluency was significantly associated with higher baseline SE but also positive change in SE, adjusting for age, sex WRAT‐3 and depression (Table 4). Conclusion: This study shows that spontaneous speech metrics are positively associated with self‐reported social engagement in late middle age. Crucial next steps include determining if SE moderates the effect of amyloid, tau, and neurodegeneration on measures of cognition including speech. SE is a potentially modifiable lifestyle behavior that may have positive implications for improved outcomes in communication and cognitive function. [ABSTRACT FROM AUTHOR]

Background: Biomarker‐related cognitive decline begins well before mild cognitive impairment (MCI), yet detecting subclinical decline is challenging. The presence of differences in AD biomarkers between cognitively unimpaired stable and declining subtypes (CU‐S and CU‐D) might indicate validity evidence for CU‐D criteria. This study examines whether brain amyloid and tau biomarkers differed across concurrent determinations of CU‐S, CU‐D and MCI/Dementia statuses using consensus conference and algorithm‐based cognitive statuses. Method: WRAP participants with >=3 Preclinical Alzheimer's Cognitive Composite (PACC3) scores, free of dementia at baseline PACC3, and >=1 positron emission tomography (PET) [C‐11]Pittsburgh Compound B (PiB) scan were included (n=269); n=215 also had [F‐18]MK‐6240 tau scans. The mean(sd) age at last cognitive assessment was 67.0(6); PET PiB and MK scans occurred within means of 1.6(2) and.8(1.1) years of cognitive assessment. Cognitive statuses of CU‐S, CU‐D, and MCI/Dementia were assigned via consensus conference review. Six algorithm‐based cognitive status indicators were also examined (3 PACC3 composite‐based and 3 based on combinations of individual tests; for all, CU‐D range = centiles [4, 16), or ∼1 to 1.75 SD below expected; see Table 1). We compared continuous Global PiB DVR and MK‐6240 regional SUVR outcomes across cognitive statuses using analysis of variance and proportions PiB+ (Global PiB DVR>1.2) across cognitive statuses using Fisher's exact tests. Significant omnibus tests (p<.05) were followed with pairwise comparisons. Result: Table 1 shows cognitive statuses by determination method. All omnibus tests comparing biomarkers across CU‐S, CU‐D and MCI groups were significant. CU‐D had higher proportion PiB+ than CU‐S for 4 of 7 methods (Figure 1). CU‐D differed from CU‐S on average Global PiB DVR (5 methods; Figure 2), MK‐6240 entorhinal cortex SUVR (6 methods, Figure 3), and MK‐6240 hippocampus SUVR (6 methods, Figure 4). Conclusion: Reaching a clinical diagnosis of Alzheimer's or other dementia is the culmination of years of brain changes and initially subtle cognitive decline. The observed increased PET amyloid and tau pathology among cognitively unimpaired‐declining participants, particularly in cognitive status determination methods using longitudinal patterns, suggest CU‐D criteria can be sensitive to preclinical changes. Detecting subclinical cognitive decline may improve opportunity for early intervention that could ultimately delay clinical impairment. [ABSTRACT FROM AUTHOR]

Background: Characterizing longitudinal changes in AD and neurodegeneration related CSF biomarkers in cognitively unimpaired adults is critical to distinguishing typical aging from disease pathology, Yet longitudinal change in established and especially emerging CSF biomarkers are not well characterized. We examined linear age trends in CSF biomarkers for AD (Ab42, pTau), neurodegeneration (NFL, Neurogranin and α‐Synuclein) and glial activation (YKL40, GFAP, sTREM2, S100B) assayed using the Roche NeuroToolKit (NTK), a panel of automated prototype CSF immunoassays. Methods: Analyses were based on a subsample of 146 cognitively unimpaired participants who underwent at least two lumbar punctures (total N=402). Cut‐offs for the ratio of pTau/Ab42 were determined using receiver operating characteristic curve analysis in a larger sample of impaired and unimpaired middle‐aged adults (N=167) treating a positive [C‐11]PiB visual rating as the standard of comparison. CSF analytes, plus ratios Ab42/Ab40 and pTau/Ab42, were fit to linear mixed models with random intercepts and slopes. Biomarker status (pTau/Ab42 +/‐) and covariates (gender, APOE4 status, years education) were entered as fixed effects. We also tested for moderation of age effects by biomarker status. P‐values were adjusted for the false discovery rate. Results: Sample characteristics are shown in Table 1. Twenty participants were AD biomarker positive. We observed small but significant subject‐level variation in slopes for most analytes, including the core AD biomarkers, NFL, neurogranin, YKL40, and S100B. Biomarker status (pTau/Ab42) significantly predicted levels of AD related CSF biomarkers (Table 2), but not levels of neurodegeneration or glial activation related analytes. Ab42/Ab40, but not Ab42, levels significantly decreased over time; all other analytes increased over time with the exception of S100B (Figure 1). Changes in biomarker values over time was not moderated by baseline biomarker status for any of the CSF analytes (not shown). Conclusion: Prior preliminary analyses (recreated in Figure 2) suggested that baseline values for biomarkers of neurodegeneration and glial activation are not related to core AD biomarkers until later in disease pathology. We have added to those findings, showing that among cognitively unimpaired individuals, significant longitudinal changes in both core AD‐related and emerging CSF biomarkers appear to be independent of early signs of joint amyloid and tau pathology. [ABSTRACT FROM AUTHOR]

Background: Insulin resistance (IR) has been related to increased risk for Alzheimer's clinical syndrome. While some cross‐sectional studies have linked IR to Alzheimer's disease (AD) pathology, others have been negative. We examined the relationship of IR to longitudinal cerebrospinal fluid (CSF) biomarkers of AD pathology, cerebral amyloid chronicity, and cognitive decline. Method: Participants (Table 1) were enrolled in the Wisconsin Registry for Alzheimer's Prevention (WRAP), a longitudinal observational study of non‐demented adults. We utilized data from biennial visits and CSF and positron emission tomography (PET) sub‐studies. For our first study, participants (n=148) had CSF biomarker data (Aβ42: Aβ40 ratio, p‐tau181, t‐tau, and neurogranin) from 1‐5 CSF samples collected every 2‐8 years and fasting insulin and glucose (collected during 1‐5 visits before first CSF) for calculating homeostatic model assessment of insulin resistance (HOMA‐IR). CSF was assayed using the Roche NeuroToolKit on the Elecsys® platform. Linear mixed effects models tested HOMA‐IR as a moderator of aging‐related change in CSF biomarkers. In study 2, participants (n=253) had [C11]PiB PET imaging and 3‐6 HOMA‐IR values for calculating a within‐person HOMA‐IR mean and SD (i.e., fluctuation). Amyloid chronicity was calculated by subtracting estimated age at PiB positivity (i.e., estimated age when PiB DVR ≥ 1.2) (Koscik & Betthauser, et al., 2020) from age at last HOMA‐IR. Hierarchical regression tested whether HOMA‐IR and an HOMA‐IR x APOE4 interaction predicted amyloid chronicity. In study 3 (n=1,298), linear mixed effects tested if baseline HOMA‐IR moderated age‐related decline in longitudinal measures of the preclinical Alzheimer's cognitive composite (PACC). Age, APOE4, sex, education, and systolic blood pressure (SBP) were controlled in all study models. Result: HOMA‐IR was not a significant moderator of aging‐related change in any CSF biomarker (Figure 1 & 2). HOMA‐IR mean and SD and the HOMA‐IR x APOE4 interaction were not significant predictors of amyloid chronicity. Increased HOMA‐IR predicted greater age‐related decline in PACC (Figure 3). Conclusion: HOMA‐IR was related to cognitive decline but not cerebral amyloid chronicity or age‐related change in CSF biomarkers of AD pathology and neurodegeneration. IR may not be directly related to preclinical AD pathology. Pathways linking IR to dementia require further investigation. [ABSTRACT FROM AUTHOR]

Background: Studies using cerebrospinal fluid (CSF) biomarkers often combine CSF samples collected at different centers or assayed in different laboratories or batches, creating a clustered data structure. Such clustering needs to be controlled in the analysis of the combined data. Intraclass correlation coefficient (ICC), i.e., the ratio of the between‐cluster variance over the total variance, is used to quantify the magnitude of the clustering. The between‐cluster variance is a mix of (a) difference in CSF samples and (b) variation in measurement (i.e., unreliability). Thus the observed ICC (under unreliability) would deviate from the true ICC (under perfect reliability). We studied (1) how the observed ICC would deviate and (2) the statistical performance in its estimation. Method: Aim (1) was achieved through mathematical derivations based on Classical Test Theory. Aim (2) was achieved using a simulation study to evaluate the relative bias, empirical standard error (se), and root mean square error (RMSE) in the sample estimates of the observed ICC, under three reliability levels (0.7, 0.8, 0.9), three true ICC values (0.1, 0.3, 0.5), and two cluster sizes (10, 50), with 10,000 replications for each condition. Result: Aim (1): The observed ICC is greater than the true ICC. The inflation, represented as their ratio, is a function of reliability, true ICC, and cluster size (Figure 1). It decreases with increased reliability or true ICC, or decreased cluster size (Figures 2, 3). Aim (2): The sample estimates had very small negative bias (within 3%), which generally decreased with increased cluster size or true ICC, or reduced reliability. The empirical se and RMSE decreased with increased cluster size, but their relations with reliability or true ICC varied across conditions (Table 1). Conclusion: The observed ICC is deviated (inflated), but unbiased. Using reliable measure is crucial when CSF samples have low variation between clusters to avoid substantial inflation in ICC. Increasing cluster size would exacerbate the inflation, yet would also increase estimation accuracy and efficiency. Such tradeoffs should be considered with planning CSF sample collection and assay. These findings can be applied to clustered data of other outcomes such as imaging and cognition. [ABSTRACT FROM AUTHOR]

Background: Aging is often accompanied by the accumulation of chronic illnesses such as diabetes, cardiovascular disease or hypertension and a corresponding increase in use of medications to manage these conditions. Polypharmacy (use of multiple prescription medications) is a growing health concern and has been associated with poorer clinical outcomes in older adults. This study investigates associations between comorbidities, polypharmacy and longitudinal cognitive performance in an Alzheimer's disease (AD) risk‐enriched cohort, the Wisconsin Registry for Alzheimer's Prevention (WRAP). Method: Eligible WRAP participants included 1039 (free of dementia or clinical impairment not due to mild cognitive impairment (MCI) and with >=2 visits with cognitive composites, self‐reported health history, and concurrent medication records. Sums of prescribed medications and self‐reported co‐morbidities (include self‐ report over the lifetime) (Table 1) were calculated for each visit and converted to z‐scores for use in mixed effects models of cognition (covariates: age, gender, education, WRAT III reading recognition, practice and study site). Linear mixed effects analyses first examined whether associations between medications, comorbidities and cognitive z‐scores varied across cognitive domains of working memory, immediate recall, delayed recall, executive function, and a preclinical Alzheimer's cognitive composite (PACC3) before examining associations with each cognitive composite. In secondary analyses, we examined whether number of medications and comorbidities interacted with each other or modified age trajectories. Result: Sample characteristics are shown in Table 2 overall and by gender (mean(sd) baseline age=58.99(6.60)). More comorbidities were associated with more prescription medications (Figure 1). More comorbidities and medications were associated with lower executive function (β = ‐0.11, p=.002; β = ‐0.07, p = 0.047; Table 3); using z‐scores of 10th and 90th centile with model parameters, those with low co‐morbidities and medications performed an average of 0.39 SD higher than those elevated on both (Figure 2). Conclusion: Associations between medications, comorbidities, and executive function suggest that persons with more comorbidities and medications may be either at increased risk of reaching clinical levels of impairment earlier than healthier, less medicated peers, or may have potentially reversible clinical impairment. Researchers should be aware of the possibility that medication use and comorbidity may impact cognitive performance (and by extension, adjudicated cognitive status). [ABSTRACT FROM AUTHOR]