Study Objectives: Given the established racial disparities in both sleep health and dementia risk for African American populations, we assess cross-sectional and longitudinal associations of self-report sleep duration (SRSD) and daytime sleepiness with plasma amyloid beta (A beta) and cognition in an African American (AA) cohort.Methods: In a cognitively unimpaired sample drawn from the African Americans Fighting Alzheimer's in Midlife (AA-FAiM) study, data on SRSD, Epworth Sleepiness Scale, demographics, and cognitive performance were analyzed. A beta 40, A beta 42, and the A beta 42/40 ratio were quantified from plasma samples. Cross-sectional analyses explored associations between baseline predictors and outcome measures. Linear mixed-effect regression models estimated associations of SRSD and daytime sleepiness with plasma A beta and cognitive performance levels and change over time.Results: One hundred and forty-seven participants comprised the cross-sectional sample. Baseline age was 63.2 +/- 8.51 years. 69.6% self-identified as female. SRSD was 6.4 +/- 1.1 hours and 22.4% reported excessive daytime sleepiness. The longitudinal dataset included 57 participants. In fully adjusted models, neither SRSD nor daytime sleepiness is associated with cross-sectional or longitudinal A beta. Associations with level and trajectory of cognitive test performance varied by measure of sleep health.Conclusions: SRSD was below National Sleep Foundation recommendations and daytime sleepiness was prevalent in this cohort. In the absence of observed associations with plasma A beta, poorer self-reported sleep health broadly predicted poorer cognitive function but not accelerated decline. Future research is necessary to understand and address modifiable sleep mechanisms as they relate to cognitive aging in AA at disproportionate risk for dementia.Clinical Trial Information Not applicable.

We sought to characterize the timing of changes in cognitive trajectories related to genetic risk using the apolipoprotein E (APOE) score, a continuous measure of Alzheimer's disease (AD) risk. We also aimed to determine whether that timing was different when genetic risk was measured using an AD polygenic risk score (PRS) that contains APOE.

IMPORTANCE Postmenopausal females represent around 70% of all individuals with Alzheimer disease. Previous literature shows elevated levels of tau in cognitively unimpaired postmenopausal females compared with age-matched males, particularly in the setting of high beta-amyloid (A beta). The biological mechanisms associated with higher tau deposition in female individuals remain elusive. OBJECTIVE To examine the extent to which sex, age at menopause, and hormone therapy (HT) use are associated with regional tau at a given level of A beta, both measured with positron emission tomography (PET). DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study included participants enrolled in the Wisconsin Registry for Alzheimer Prevention. Cognitively unimpaired males and females with at least 1 18F-MK-6240 and 11C-Pittsburgh compound B PET scan were analyzed. Data were collected between November 2006 and May 2021. EXPOSURES Premature menopause (menopause at younger than 40 years), early menopause (menopause at age 40-45 years), and regular menopause (menopause at older than 45 years) and HT user (current/past use) and HT nonuser (no current/past use). Exposures were self-reported. MAIN OUTCOMES AND MEASURES Seven tau PET regions that showsex differences across temporal, parietal, and occipital lobes. Primary analyses examined the interaction of sex, age at menopause or HT, and A beta PET on regional tau PET in a series of linear regressions. Secondary analyses investigated the influence of HT timing in association with age at menopause on regional tau PET. RESULTS Of 292 cognitively unimpaired individuals, there were 193 females (66.1%) and 99 males (33.9%). The mean (range) age at tau scan was 67 (49-80) years, 52 (19%) had abnormal A beta, and 106 (36.3%) were APOEe4 carriers. There were 98 female HT users (52.2%) (past/current). Female sex (standardized beta = -0.41; 95% CI, -0.97 to -0.32; P <.001), earlier age at menopause (standardized beta = -0.38; 95% CI, -0.14 to -0.09; P <.001), and HT use (standardized beta = 0.31; 95% CI, 0.40-1.20; P =.008) were associated with higher regional tau PET in individuals with elevated A beta compared with male sex, later age at menopause, and HT nonuse. Affected regions included medial and lateral regions of the temporal and occipital lobes. Late initiation of HT (>5 years following age at menopause) was associated with higher tau PET compared with early initiation (beta = 0.49; 95% CI, 0.27-0.43; P =.001). CONCLUSIONS AND RELEVANCE In this study, females exhibited higher tau compared with age-matched males, particularly in the setting of elevated A beta. In females, earlier age at menopause and late initiation of HT were associated with increased tau vulnerability especially when neocortical A beta elevated. These observational findings suggest

OBJECTIVE:Self-perceived cognitive functioning, considered highly relevant in the context of aging and dementia, is assessed in numerous ways-hindering the comparison of findings across studies and settings. Therefore, the present study aimed to link item-level self-report questionnaire data from international aging studies. METHOD:We harmonized secondary data from 24 studies and 40 different questionnaires with item response theory (IRT) techniques using a graded response model with a Bayesian estimator. We compared item information curves to identify items with high measurement precision at different levels of the self-perceived cognitive functioning latent trait. Data from 53,030 neuropsychologically intact older adults were included, from 13 English language and 11 non-English (or mixed) language studies. RESULTS:We successfully linked all questionnaires and demonstrated that a single-factor structure was reasonable for the latent trait. Items that made the greatest contribution to measurement precision (i.e., "top items") assessed general and specific memory problems and aspects of executive functioning, attention, language, calculation, and visuospatial skills. These top items originated from distinct questionnaires and varied in format, range, time frames, response options, and whether they captured ability and/or change. CONCLUSIONS:This was the first study to calibrate self-perceived cognitive functioning data of geographically diverse older adults. The resulting item scores are on the same metric, facilitating joint or pooled analyses across international studies. Results may lead to the development of new self-perceived cognitive functioning questionnaires guided by psychometric properties, content, and other important features of items in our item bank. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Sleep abnormalities may play a prominent role in the pathogenesis or exacerbation of Alzheimer’s disease (AD). Sleep is considered a modifiable target for prevention and treatment. Racial sleep health disparities exist, and may contribute to the disproportionately increased risk for developing AD associated with being African American. Yet, the relationships between sleep health and AD pathophysiology in African American individuals are under-studied. This investigation was designed to address research gaps by evaluating the associations of self-report sleep duration, sleep disturbances, and daytime sleepiness with change in plasma-derived amyloid β 42/40 ratio (Aβ42/40) in an African American sample. Archival data from African American participants enrolled in the Wisconsin Registry for Alzheimer’s Prevention study and the Wisconsin Alzheimer’s Disease Research Center were utilized. Data included the Medical Outcomes Study Sleep Scale (MOS-Sleep), Epworth Sleepiness Scale (ESS), and demographic information. Sleep duration was determined by MOS-Sleep #2, sleep disturbances were captured by a related MOS-Sleep subscale (SLPD4), and ESS assessed daytime sleepiness. Participants provided plasma samples across multiple timepoints. PrecivityAD TM assays quantified Aβ42/40. Aβ42/40 rate of change (Aβ42/40_ROC) was computed for follow-up collections to serve as the primary outcome measure. Aβ42/40_ROC was computed as the difference in Aβ42/40 between follow-up and first collection divided by years between collections. Linear mixed-effects models determined associations with Aβ42/40_ROC. Observations were nested within participant. Fully adjusted models included age at collection, gender, BMI, education, parental history of dementia, and APOEe4 carrier status. Data included 339 follow-up collections from 143 participants. Women provided 70.8% of collections, with average age at collection = 66.6 ± 8.84 years, Aβ42/40_ROC = -0.0005 ± 0.003, and sleep duration = 6.35 ± 1.00 hours. Sleep duration significantly associated with Aβ42/40_ROC in the fully adjusted model (β = -0.0005; SE = 0.0002; p = 0.02) (Figure 1). Associations between SLPD4 and ESS with Aβ42/40_ROC were nonsignificant. Longer self-report sleep duration associated with a steeper reduction rate for plasma Aβ42/40, a biomarker for amyloid pathology. Further research is necessary to clarify the relationships between sleep health components and changes in AD-related biomarkers within African American adults to identify focal risk factors and interventional strategies.

BACKGROUND:Insulin resistance (IR) and type 2 diabetes have been found to increase the risk for Alzheimer's clinical syndrome in epidemiologic studies but have not been associated with tau tangles in neuropathological research and have been inconsistently associated with cerebrospinal fluid P-tau181. IR and type 2 diabetes are well-recognized vascular risk factors. Some studies suggest that cardiovascular risk may act synergistically with cortical amyloid to increase tau measured using tau PET. Utilizing data from largely nondemented middle-aged and older adult cohorts enriched for AD risk, we investigated the association of IR and diabetes to tau PET and whether amyloid moderated those relationships.METHODS:Participants were enrolled in either the Wisconsin Registry for Alzheimer's Prevention (WRAP) or Wisconsin Alzheimer's Disease Research Center (WI-ADRC) Clinical Core. Two partially overlapping samples were studied: a sample characterized using HOMA-IR (n=280 WRAP participants) and a sample characterized on diabetic status (n=285 WRAP and n=109 WI-ADRC). IR was measured using the homeostasis model assessment of insulin resistance (HOMA-IR). Tau PET employing the radioligand 18F-MK-6240 was used to detect AD-specific aggregated tau. Linear regression tested the relationship of IR and diabetic status to tau PET standardized uptake value ratio (SUVR) within the entorhinal cortex and whether relationships were moderated by amyloid assessed by amyloid PET distribution volume ratio (DVR) and amyloid PET positivity status.RESULTS:Neither HOMA-IR nor diabetic status was significantly associated with tau PET SUVR. The relationship between IR and tau PET SUVR was not moderated by amyloid PET DVR or positivity status. The association between diabetic status and tau PET SUVR was not significantly moderated by amyloid PET DVR but was significantly moderated by amyloid PET positivity status. Among the amyloid PET-positive participants, the estimated marginal tau PET SUVR mean was higher in the diabetic (n=6) relative to the nondiabetic group (n=88).CONCLUSION:Findings indicate that IR may not be related to tau in generally healthy middle-aged and older adults who are in the early stages of the AD clinicopathologic continuum but suggest the need for additional research to investigate whether a synergistic relationship between type 2 diabetes and amyloid is associated with increased tau levels.

(1) Smoking is the most significant preventable health hazard in the modern world. It increases the risk of vascular problems, which are also risk factors for dementia. In addition, toxins in cigarettes increase oxidative stress and inflammation, which have both been linked to the development of Alzheimer’s disease and related dementias (ADRD). This study identified potential mechanisms of the smoking–cognitive function relationship using metabolomics data from the longitudinal Wisconsin Registry for Alzheimer’s Prevention (WRAP). (2) 1266 WRAP participants were included to assess the association between smoking status and four cognitive composite scores. Next, untargeted metabolomic data were used to assess the relationships between smoking and metabolites. Metabolites significantly associated with smoking were then tested for association with cognitive composite scores. Total effect models and mediation models were used to explore the role of metabolites in smoking-cognitive function pathways. (3) Plasma N-acetylneuraminate was associated with smoking status Preclinical Alzheimer Cognitive Composite 3 (PACC3) and Immediate Learning (IMM). N-acetylneuraminate mediated 12% of the smoking-PACC3 relationship and 13% of the smoking-IMM relationship. (4) These findings provide links between previous studies that can enhance our understanding of potential biological pathways between smoking and cognitive function.

Objective: The preeminent in vivo cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) are amyloid beta 1-42 (A1342), phosphorylated Tau (p-tau), and total Tau (t-tau). The goal of this study was to examine how well traditional (total and delayed recall) and process-based (recency ratio [Rr]) measures derived from Rey's Auditory Verbal Learning test (AVLT) were associated with these biomarkers. Method: Data from 235 participants (M-age = 65.5, SD = 6.9), who ranged from cognitively unimpaired to mild cognitive impairment, and for whom CSF values were available, were extracted from the Wisconsin Registry for Alzheimer's Prevention. Bayesian regression analyses were carried out using CSF scores as outcomes, AVLT scores as predictors, and controlling for demographic data and diagnosis. Results: We found moderate evidence that Rr was associated with both CSF p-tau (Bayesian factor [BFM] = 5.55) and t-tau (BFM = 7.28), above and beyond the control variables, while it did not correlate with CSF A1342 levels. In contrast, total and delayed recall scores were not linked with any of the AD biomarkers, in separate analyses. When comparing all memory predictors in a single regression, Rr remained the strongest predictor of CSF t-tau levels (BFM = 3.57). Conclusions: Our findings suggest that Rr may be a better cognitive measure than commonly used AVLT scores to assess CSF levels of p-tau and t-tau in nondemented individuals.

Depressive symptomatology is associated with cognitive decline and greater risk for dementia. Studies have examined this association using self-report measures such as the Centre for Epidemiologic Studies-Depression Scale (CES-D; Radloff, 1991). Although the CES-D was originally designed as a 20-item scale, certain items have been reported to misrepresent depressive symptoms due to sex and cultural differences, which are addressed in the 14-item version of this measure (Carleton et al., 2013). The aim of this study was to examine how the CES-D total score from the 14-item version compared to the 20-item version in predicting progression to cognitive decline from a cognitively unimpaired baseline, across genders. Data were extracted from the Wisconsin Registry for Alzheimer’s Prevention. A total of 1,055 participants were included who were cognitively unimpaired and stable at baseline and had at least one follow-up assessment. Bivariate logistic regression analyses were conducted using follow-up consensus diagnosis as outcome (cognitively unimpaired stable vs. cognitive unimpaired declining, mild cognitive impairment or dementia); baseline total CES-D scores from either the 14-item or 20-item version were used as predictors in separate analyses; age at last follow-up assessment, age difference between baseline and last follow-up assessment, years of education, and polygenic risk score were covariates (see Table 1). In female participants, we observed that the logistic regression model with the total CES-D score from the 14-item version was statistically significant (χ2(5) = 54.674, p = .001) and explained 15.2% (Nagelkerke R 2 ) of the variance in predicting cognitive decline; this result was comparable to that with the 20-item version, which was also statistically significant (χ2(5) = 53.276, p = .001) and explained 14.8% of the variance. In male participants, the model with the total score from the 14-item version was statistically significant (χ2(5) = 14.846, p = .011) and explained 9.9% of the variance, whereas the model with the total score from the 20-item version was statistically significant (χ2(5) = 14.070, p = .015) and explained 9.4% of the variance. Our findings suggest that the total CES-D score from the 14-item version is comparable to, if not slightly better than, the score from the 20-item version in predicting progression to a clinical level of cognitive decline in both males and females.

In this study we investigated cerebrovascular health and vascular contributions to cognitive impairment in the presence and absence of amyloidosis. We employed various neuroimaging techniques including 4D-Flow, multi-delay ASL, T2-FLAIR, and structural T1 for vascular and structural biomarkers. β-amyloid (Aβ) burden was determined from PET imaging data. Data supports the notion that vascular dysfunction occurs in the presence and absence of Aβ, albeit with differing manifestations leading to cognitive decline.

Introduction:Research focusing on cognitive aging and dementia is a global endeavor. However, cross-national differences in cognition are embedded in other sociocultural differences, precluding direct comparisons of test scores. Such comparisons can be facilitated by co-calibration using item response theory (IRT). The goal of this study was to explore, using simulation, the necessary conditions for accurate harmonization of cognitive data. Method:Neuropsychological test scores from the US Health and Retirement Study (HRS) and the Mexican Health and Aging Study (MHAS) were subjected to IRT analysis to estimate item parameters and sample means and standard deviations. These estimates were used to generate simulated item response patterns under 10 scenarios that adjusted the quality and quantity of linking items used in harmonization. IRT-derived factor scores were compared to the known population values to assess bias, efficiency, accuracy, and reliability of the harmonized data. Results:The current configuration of HRS and MHAS data was not suitable for harmonization, as poor linking item quality led to large bias in both cohorts. Scenarios with more numerous and higher quality linking items led to less biased and more accurate harmonization. Discussion:Linking items must possess low measurement error across the range of latent ability for co-calibration to be successful. HIGHLIGHTS:We developed a statistical simulation platform to evaluate the degree to which cross-sample harmonization accuracy varies as a function of the quality and quantity of linking items.Two large studies of aging-one in Mexico and one in the United States-use three common items to measure cognition.These three common items have weak correspondence with the ability being measured and are all low in difficulty.Harmonized scores derived from the three common linking items will provide biased and inaccurate estimates of cognitive ability.Harmonization accuracy is greatest when linking items vary in difficulty and are strongly related to the ability being measured.

BACKGROUND:Prior research suggests a link between menopausal hormone therapy (MHT) use, memory function, and diabetes risk. The menopausal transition is a modifiable period to enhance long-term health and cognitive outcomes, although studies have been limited by short follow-up periods precluding a solid understanding of the lasting effects of MHT use on cognition.OBJECTIVE:We examined the effects of midlife MHT use on subsequent diabetes incidence and late life memory performance in a large, same-aged, population-based cohort. We hypothesized that the beneficial effects of MHT use on late life cognition would be partially mediated by reduced diabetes risk.METHODS:1,792 women from the Wisconsin Longitudinal Study (WLS) were included in analysis. We employed hierarchical linear regression, Cox regression, and causal mediation models to test the associations between MHT history, diabetes incidence, and late life cognitive performance.RESULTS:1,088/1,792 women (60.7%) reported a history of midlife MHT use and 220/1,792 (12.3%) reported a history of diabetes. MHT use history was associated with better late life immediate recall (but not delayed recall), as well as a reduced risk of diabetes with protracted time to onset. Causal mediation models suggest that the beneficial effect of midlife MHT use on late life immediate recall were at least partially mediated by diabetes risk.CONCLUSION:Our data support a beneficial effect of MHT use on late life immediate recall (learning) that was partially mediated by protection against diabetes risk, supporting MHT use in midlife as protective against late life cognitive decline and adverse health outcomes.

Background: Genetic scores for late-onset Alzheimer’s disease (LOAD) have been associated with preclinical cognitive decline and biomarker variations. Compared with an overall polygenic risk score (PRS), a pathway-specific PRS (p-PRS) may be more appropriate in predicting a specific biomarker or cognitive component underlying LOAD pathology earlier in the lifespan. Objective: In this study, we leveraged longitudinal data from the Wisconsin Registry for Alzheimer’s Prevention and explored changing patterns in cognition and biomarkers at various age points along six biological pathways. Methods: PRS and p-PRSs with and without APOE were constructed separately based on the significant SNPs associated with LOAD in a recent genome-wide association study meta-analysis and compared to APOE alone. We used a linear mixed-effects model to assess the association between PRS/p-PRSs and cognitive trajectories among 1,175 individuals. We also applied the model to the outcomes of cerebrospinal fluid biomarkers in a subset. Replication analyses were performed in an independent sample. Results: We found p-PRSs and the overall PRS can predict preclinical changes in cognition and biomarkers. The effects of PRS/p-PRSs on rate of change in cognition, amyloid-β, and tau outcomes are dependent on age and appear earlier in the lifespan when APOE is included in these risk scores compared to when APOE is excluded. Conclusion: In addition to APOE, the p-PRSs can predict age-dependent changes in amyloid-β, tau, and cognition. Once validated, they could be used to identify individuals with an elevated genetic risk of accumulating amyloid-β and tau, long before the onset of clinical symptoms.

Background Alzheimer’s disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neuroimaging have been limited. Sensitive markers that may account for or predict cognitive dysfunction for individuals in early disease stages are critical. Methods Participants ( n = 296) defined on A and T status and spanning the AD-clinical continuum underwent multi-shell diffusion-weighted magnetic resonance imaging to generate Neurite Orientation Dispersion and Density Imaging (NODDI) metrics, which were tested as markers of N. To better define N, we developed age- and sex-adjusted robust z -score values to quantify normal and AD-associated (abnormal) neurodegeneration in both cortical gray matter and subcortical white matter regions of interest. We used general logistic regression with receiver operating characteristic (ROC) and area under the curve (AUC) analysis to test whether NODDI metrics improved diagnostic accuracy compared to models that only relied on cerebrospinal fluid (CSF) A and T status (alone and in combination). Results Using internal robust norms, we found that NODDI metrics correlate with worsening cognitive status and that NODDI captures early, AD neurodegenerative pathology in the gray matter of cognitively unimpaired, but A/T biomarker-positive, individuals. NODDI metrics utilized together with A and T status improved diagnostic prediction accuracy of AD clinical status, compared with models using CSF A and T status alone. Conclusion Using a robust norms approach, we show that abnormal AD-related neurodegeneration can be detected among cognitively unimpaired individuals. Metrics derived from diffusion-weighted imaging are potential sensitive markers of N and could be considered for trial enrichment and as outcomes in clinical trials. However, given the small sample sizes, the exploratory nature of the work must be acknowledged.

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. 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-tau 181 /amyloid-beta 1-42 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/Aβ 42 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. 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. 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.

Background Characterizing cerebrovascular hemodynamics in older adults is important for identifying disease and understanding normal neurovascular aging. Four-dimensional (4D) flow MRI allows for a comprehensive assessment of cerebral hemodynamics in a single acquisition. Purpose To establish reference intracranial blood flow and pulsatility index values in a large cross-sectional sample of middle-aged (45-65 years) and older (>65 years) adults and characterize the effect of age and sex on blood flow and pulsatility. Materials and Methods In this retrospective study, patients aged 45-93 years (cognitively unimpaired) underwent cranial 4D flow MRI between March 2010 and March 2020. Blood flow rates and pulsatility indexes from 13 major arteries and four venous sinuses and total cerebral blood flow were collected. Intraobserver and interobserver reproducibility of flow and pulsatility measures was assessed in 30 patients. Descriptive statistics (mean ± SD) of blood flow and pulsatility were tabulated for the entire group and by age and sex. Multiple linear regression and linear mixed-effects models were used to assess the effect of age and sex on total cerebral blood flow and vessel-specific flow and pulsatility, respectively. Results There were 759 patients (mean age, 65 years ± 8 [SD]; 506 female patients) analyzed. For intra- and interobserver reproducibility, median intraclass correlation coefficients were greater than 0.90 for flow and pulsatility measures across all vessels. Regression coefficients β ± standard error from multiple linear regression showed a 4 mL/min decrease in total cerebral blood flow each year (age β = -3.94 mL/min per year ± 0.44; P < .001). Mixed effects showed a 1 mL/min average annual decrease in blood flow (age β = -0.95 mL/min per year ± 0.16; P < .001) and 0.01 arbitrary unit (au) average annual increase in pulsatility over all vessels (age β = 0.011 au per year ± 0.001; P < .001). No evidence of sex differences was observed for flow (β = -1.60 mL/min per male patient ± 1.77; P = .37), but pulsatility was higher in female patients (sex β = -0.018 au per male patient ± 0.008; P = .02). Conclusion Normal reference values for blood flow and pulsatility obtained using four-dimensional flow MRI showed correlations with age. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Steinman in this issue.

Cognitive decline in Alzheimer’s disease (AD) and other dementias may accelerate in preclinical phases as brain pathology increases. Here we: 1) developed cognitive trajectory profiles using longitudinally-based random slope and change point (CP) parameter estimates from a cognitive composite; and 2) examined how AD-related biomarkers varied across these cognitive profiles. WRAP participants with > = 3 Preclinical Alzheimer’s Cognitive Composite (PACC3) scores, dementia-free at baseline, were included (n = 1068). Amyloid measures from positron emission tomography (PET) [C-11]Pittsburgh Compound B (PiB; n = 361) scans included Global PiB DVR and proportion PiB+ (Global PiB DVR>1.16). Tau measures included PET MK-6240 regional entorhinal cortex and hippocampal SUVR (n = 321). Neurodegeneration measures included MRI hippocampal volume and global brain atrophy (GBA; n = 581). Plasma measures included pTau217 (n = 166). Posterior median estimate person-level CPs, slopes pre- and post-CP, and intercepts at CP for PACC3 were extracted from Bayesian random CP mixed models (BRCPMM; age = time scale) and used to characterize cognitive trajectory profiles (K-means clustering). We compared demographic, last visit cognitive statuses (cognitively unimpaired-stable (CU-S), CU-declining (CU-D), and MCI/Dementia), amyloid, tau, neurodegenerations and plasma measures across the cognitive trajectory profiles using analysis of variance, chi-square and Fisher’s exact tests. Significant omnibus tests (p<.05) were followed with pairwise comparisons. Mean(sd) last cognitive assessment age was 66.6(6.6) years; PiB, MK, MRI scans and plasma occurred within mean(sd) 1.1(2.7), 1.6(1.4), .6(3) and .09(.7) years of cognitive assessment. Cluster analysis identified 3 groups of performance patterns representing highest to lowest risk of cognitive decline (high: n = 77(7.2%); intermediate: n = 446(41.8%); and low: n = 545(51.0%); Figure 1 & 2). The high risk group was older, had more females, APOE e4 carriers, and MCI/Dementia at last visit (Table 1). The high risk group also had worse PiB-amyloid, MK-tau, pTau217, and MRI-neurogeneration levels than the lower risk groups (Table2; Figure 3). In this initially non-demented sample, differences between cognitive clusters across multiple biomarkers and AD risk factors indicate that within-person PACC3 performance patterns are sensitive to preclinical change. Identifying cognitive trajectory profiles may provide an opportunity for early intervention for high-risk subjects at the right time for treatment and/or enrollment in a clinical trial.

The present study investigated: 1) sex differences in polypharmacy, comorbidities, self-rated current health (SRH), and cognitive performance, 2) associations between comorbidities, polypharmacy, SRH, and objective measures of health, and 3) associations of these factors with longitudinal cognitive performance. Analyses included 1039 eligible Wisconsin Registry for Alzheimer’s Prevention (WRAP) participants who were cognitively unimpaired at baseline and had ≥2 visits with cognitive composites, self-reported health history, and concurrent medication records. Repeated measures correlation (rmcorr) examined the associations between medications, co-morbidities, SRH, and objective measures of health (including LIfestyle for BRAin Health Index (LIBRA), and depression). Linear mixed-effect models examined associations between medications, co-morbidities, and cognitive change over time using a preclinical Alzheimer’s cognitive composite (PACC3) and cognitive domain z-scores (executive function, working memory, immediate learning, and delayed recall). In secondary analyses, we also examined whether the number of medications interacted with co-morbidities and whether they modified age-related cognitive trajectories. The number of prescribed medications was associated with worse SRH and a higher number of self-reported co-morbidities. More prescribed medications were associated with a faster decline in executive function, and more comorbidities were associated with faster PACC3 decline. Those with a non-elevated number of co-morbidities and medications performed an average of 0.26 SD higher (better) in executive function and an average of 0.18 SD higher on PACC3 than those elevated on both. Associations between medications, co-morbidities, and executive function, and PACC3 suggest that persons with more co-morbidities and medications may be at increased risk of reaching clinical levels of impairment earlier than healthier, less medicated peers.

Blood biomarkers indicative of Alzheimer’s disease (AD) pathology are altered in both preclinical and symptomatic stages of the disease. Distinctive biomarkers may be optimal for the identification of AD pathology or monitoring of disease progression. Blood biomarkers that correlate with changes in cognition and atrophy during the course of the disease could be used in clinical trials to identify successful interventions and thereby accelerate the development of efficient therapies. When disease-modifying treatments become approved for use, efficient blood-based biomarkers might also inform on treatment implementation and management in clinical practice. In the BioFINDER-1 cohort, plasma phosphorylated (p)-tau231 and amyloid-β42/40 ratio were more changed at lower thresholds of amyloid pathology. Longitudinally, however, only p-tau217 demonstrated marked amyloid-dependent changes over 4–6 years in both preclinical and symptomatic stages of the disease, with no such changes observed in p-tau231, p-tau181, amyloid-β42/40, glial acidic fibrillary protein or neurofilament light. Only longitudinal increases of p-tau217 were also associated with clinical deterioration and brain atrophy in preclinical AD. The selective longitudinal increase of p-tau217 and its associations with cognitive decline and atrophy was confirmed in an independent cohort (Wisconsin Registry for Alzheimer’s Prevention). These findings support the differential association of plasma biomarkers with disease development and strongly highlight p-tau217 as a surrogate marker of disease progression in preclinical and prodromal AD, with impact for the development of new disease-modifying treatments.

Background and Objectives An accurate blood test for Alzheimer’s disease (AD) that is sensitive to preclinical proteinopathy and cognitive decline has clear implications for early detection and secondary prevention of AD. We assessed the performance of plasma pTau against brain PET markers of amyloid ([11C]-PiB) and tau ([18F]MK-6240), and its utility for predicting longitudinal cognition.Methods Samples were analyzed from a subset of participants with up to 8 years follow-up in the Wisconsin Registry for Alzheimer’s Prevention (WRAP; 2001-present; plasma 2011-present), a longitudinal cohort study of adults from midlife, enriched for parental history of AD. Participants were a convenience sample who volunteered for at least one PiB scan, had usable banked plasma, and were cognitively unimpaired at first plasma collection. Study personnel who interacted with participants or samples were blind to amyloid status. We used mixed effects models and receiver-operator characteristic curves to assess concordance between plasma pTau217 and PET biomarkers of AD, and mixed effects models to understand the ability of plasma pTau217 to predict longitudinal performance on WRAP’s preclinical Alzheimer’s cognitive composite (PACC-3).Results The primary analysis included 165 people (108 women; mean age=62.9 ± 6.06; 160 still enrolled; 2 deceased; 3 discontinued). Plasma pTau217 was strongly related to PET-based estimates of concurrent brain amyloid (β̂ DVR = 0.83 (0.75, 0.90), p<.001). Concordance was high between plasma pTau217 and both amyloid PET (AUC=0.91, specificity=0.80, sensitivity=0.85, PPV=0.58, NPV=0.94, LR −=5.48) and tau PET (AUC=0.95, specificity=1, sensitivity=0.85, PPV=1, NPV=0.98, LR −=6.47). Higher baseline pTau217 levels were associated with worse cognitive trajectories (β̂ pTau=age = -0.07 (-0.09, -0.06), p<.001).Conclusions and Relevance In a convenience sample of unimpaired adults, plasma pTau217 levels correlate well with concurrent brain AD pathophysiology and with prospective cognitive performance. These data indicate that this marker can detect AD before clinical signs and thus may disambiguate presymptomatic AD from normal cognitive aging.Classification of Evidence This study meets Class III evidential criteria for diagnostic accuracy of plasma pTau217.### Competing Interest StatementOH has acquired research support (for the institution) from ADx, AVID Radiopharmaceuticals, Biogen, Eli Lilly, Eisai, Fujirebio, GE Healthcare, Pfizer, and Roche. In the past 2 years, he has received consultancy/speaker fees from AC Immune, Amylyx, Alzpath, BioArctic, Biogen, Cerveau, Fujirebio, Genentech, Novartis, Roche, and Siemens. SCJ has served as a consultant to Eisai and Roche Diagnostics, has received an equipment grant from Roche Diagnostics, and has received support (sponsoring of an observational study and provision of precursor for tau imaging) from Cerveau Technologies. Authors EMJ, SJ, KAC, RLK, LD, NAC, NMC, KJH, BTC, and TJB have nothing to disclose. Work at the University of Wisconsin was supported by NIH R01AG027161 (Johnson), NIH RO1AG021155 (Johnson), AARF 19614533 (Betthauser), S10 OD025245-01 (Christian) and the University of Wisconsin Institute for Clinical and Translational Research NIH UL1TR002375 (Cody). We extend our deepest thanks to the WRAP participants and staff for their invaluable contributions to the study. Work at Lund University was supported by the Swedish Research Council (2016-00906), the Knut and Alice Wallenberg foundation (2017-0383), the Marianne and Marcus Wallenberg foundation (2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson's disease) at Lund University, the Swedish Alzheimer Foundation (AF-939932), the Swedish Brain Foundation (FO2021-0293), The Parkinson foundation of Sweden (1280/20), the Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, the Skåne University Hospital Foundation (2020-O000028), Regionalt Forskningsstöd (2020-0314) and the Swedish federal government under the ALF agreement (2018-Projekt0279).### Funding StatementOH has acquired research support (for the institution) from ADx, AVID Radiopharmaceuticals, Biogen, Eli Lilly, Eisai, Fujirebio, GE Healthcare, Pfizer, and Roche. In the past 2 years, he has received consultancy/speaker fees from AC Immune, Amylyx, Alzpath, BioArctic, Biogen, Cerveau, Fujirebio, Genentech, Novartis, Roche, and Siemens. SCJ has served as a consultant to Eisai and Roche Diagnostics, has received an equipment grant from Roche Diagnostics, and has received support (sponsoring of an observational study and provision of precursor for tau imaging) from Cerveau Technologies. Authors EMJ, SJ, KAC, RLK, LD, NAC, NMC, KJH, BTC, and TJB have nothing to disclose. Work at the University of Wisconsin was supported by NIH R01AG027161 (Johnson), NIH RO1AG021155 (Johnson), AARF 19614533 (Betthauser), S10 OD025245-01 (Christian) and the University of Wisconsin Institute for Clinical and Translational Research NIH UL1TR002375 (Cody). We extend our deepest thanks to the WRAP participants and staff for their invaluable contributions to the study. Work at Lund University was supported by the Swedish Research Council (2016-00906), the Knut and Alice Wallenberg foundation (2017-0383), the Marianne and Marcus Wallenberg foundation (2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson's disease) at Lund University, the Swedish Alzheimer Foundation (AF-939932), the Swedish Brain Foundation (FO2021-0293), The Parkinson foundation of Sweden (1280/20), the Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, the Skåne University Hospital Foundation (2020-O000028), Regionalt Forskningsstöd (2020-0314) and the Swedish federal government under the ALF agreement (2018-Projekt0279).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Health Sciences IRB of University of Wisconsin-Madison gave ethical approval for this workI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors.