Lundt, Emily S.

BACKGROUND: White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease but reflect late-stage injury. Diffusion magnetic resonance imaging (MRI) biomarkers have been proposed to capture earlier small vessel disease-related microstructural damage but their temporal progression relative to WMH and risk factors associated with progression remain unexplored. METHODS: We identified 2077 participants from the population-based cohort study of the Mayo Clinic Study of Aging in Olmsted County, Minnesota (aged 50–101 years) collected between 05/2005 and 09/2024 with longitudinal neuroimaging. Using multioutput nonlinear mixed-effects models in those with at least 2 fluid-attenuated inversion recovery-MRI and diffusion MRI scans, we characterized the temporal progression of WMH and 4 diffusion MRI biomarkers: fractional anisotropy of the genu of the corpus callosum, peak width of skeletonized mean diffusivity, free water, and Arteriolosclerosis-score, which were automatically estimated. Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, cardiometabolic conditions). RESULTS: The study population had a mean age of 78 years, 47% were women, 28% were APOE ε4 allele carriers, and 80% were cognitively unimpaired, with an average follow-up of 5.2 years (SD, 4.3 years) for fluid-attenuated inversion recovery-MRI and 4.3 years (SD, 3.9 years) for diffusion MRI. Arteriolosclerosis-score, fractional anisotropy of the genu of the corpus callosum, free water, and peak width of skeletonized mean diffusivity became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH become abnormal (half-width of CI <1 year), respectively. Global markers (Arteriolosclerosis-score, free water, peak width of skeletonized mean diffusivity, and WMH) were correlated, indicating shared substrates of widespread white matter injury. Fractional anisotropy of the genu of the corpus callosum, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Female participants showed earlier WMH, fractional anisotropy of the genu of the corpus callosum, and Arteriolosclerosis-score abnormalities, whereas male participants exhibited earlier peak width of skeletonized mean diffusivity and free water abnormalities. CONCLUSIONS: Diffusion MRI biomarkers were abnormal at least a decade before WMH become abnormal in the population, revealing a prolonged phase of early small vessel disease and highlighting their potential for small vessel disease prevention.

Abstract INTRODUCTION White matter lesions on fluid‐attenuated inversion recovery magnetic resonance imaging (FLAIR‐MRI) reflect small vessel disease (SVD) and aid in identifying vascular contributions to impairment and dementia (VCID). Emerging diffusion MRI (dMRI) biomarkers are sensitive to early SVD but the lack of validated approaches for defining dMRI abnormality has limited their clinical utility. METHODS In 3934 participants (8749 observations) from a population‐based sample, we classified 9% as vascular cognitive impairment versus not (VCI+/VCI−) using abnormal FLAIR‐MRI and cognitive testing. We evaluated four distinct dMRI positivity criteria and assessed performance in an independent clinical sample. RESULTS The young referent criterion performed best and yielded an estimated prevalence of dMRI positivity aligned with pathology literature. About 40% of the population were dMRI positive, and these individuals had lower baseline cognition and faster longitudinal decline. Cutpoints were validated in an independent clinical sample. DISCUSSION The proposed dMRI abnormality framework can detect VCID and aid in identification of individuals at risk of VCI.

Abstract Background White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease (SVD) but reflect late-stage injury. Diffusion MRI based biomarkers have been proposed to capture earlier SVD-related microstructural damage but their temporal progression relative to WMH and the risk factors associated with this progression have not been explored. Methods We analyzed longitudinal neuroimaging data from 2,047 participants from a population-based cohort study (aged 49-101 years, 47% female). Using multi-output nonlinear mixed-effects models, we characterized the temporal progression of WMH and four diffusion MRI based biomarkers: fractional anisotropy of the genu of the corpus callosum (Genu-FA), peak width of skeletonized mean diffusivity (PSMD), free water (FW), and Arteriolosclerosis-score (ARTS). Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, and cardiometabolic conditions). Results ARTS, Genu-FA, FW, and PSMD became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH, respectively. Global markers (ARTS, FW, PSMD, WMH) were correlated, indicating shared substrates of widespread white matter injury. Genu-FA, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Females showed earlier WMH, Genu-FA, and ARTS abnormalities whereas males exhibited earlier PSMD and FW abnormalities. Conclusions Diffusion MRI based biomarkers capture microstructural injury at least a decade before appearance of WMH, revealing a prolonged phase of early SVD and highlighting their potential for SVD prevention.

Abstract Associations of Alzheimer’s disease biomarker progression with cognitive decline are important to inform patient prognosis. Of particular interest is how newly available plasma biomarkers evolve relative to cognitive decline. The goals of this work are to measure how much earlier versus later an individual’s progression on plasma and PET Alzheimer’s disease biomarkers is associated with earlier versus later cognitive progression and to estimate the average timeline of progression of these processes in the population. In this cohort study of 2369 Mayo Clinic Study of Aging (MCSA) and 1591 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants, we fit non-linear mixed-effects models to estimate how much earlier versus later each individual progresses on plasma phosphorylated tau (p-tau)217, amyloid PET, tau PET and auditory verbal learning test (AVLT) sum of trials relative to the population mean (individual adjustment), the associations of these individual adjustments among biomarker pairs and how covariates affect the timing of biomarker progression. The association of individual adjustments implies mechanistic associations and the amount of variability in cognitive decline accounted for by each biomarker. By applying cut-off points, we also estimated the relative timing that these biomarkers become abnormal in the population. Associations of individual adjustments were moderate between all biomarkers and AVLT (R = 0.38–0.47) in the MCSA and stronger (R = 0.74–0.81) in ADNI; plasma p-tau217 accounted for 16% of the variability in timing of AVLT decline in the MCSA and 64% in ADNI. APOE ɛ4 carriership was associated with earlier biomarker progression. AVLT became abnormal after the biomarkers up to age 90, after which AVLT was estimated to become abnormal prior to tau biomarkers. The association of the timing of plasma and PET Alzheimer’s disease biomarker progression with cognitive decline was modest in the MCSA population-based sample and stronger in the Alzheimer’s disease-enriched ADNI cohort. The timing of plasma p-tau217 progression explained a similar degree of variability in AVLT progression as amyloid PET, supporting its utility as a marker of disease progression. The estimated temporal ordering of biomarkers and cognitive abnormality was as anticipated (amyloid, tau, cognition) up to the age of 90, beyond which AVLT was estimated to become abnormal prior to tau biomarkers, likely related to the effects of non-Alzheimer’s disease co-pathologies.

Abstract INTRODUCTION Understanding neuroinflammation across the Alzheimer's disease (AD) spectrum is essential to elucidate disease mechanisms and individualize therapy. METHODS Human microglial translocator protein (TSPO) expression under inflammatory stimuli was assessed by immunoblot experiments. Ninety‐six participants were enrolled across four groups: cognitively unimpaired amyloid (CU A) + and −, mild cognitive impaired (MCI) A+, and Alzheimer's disease dementia (ADD) A+. Neuroinflammation using 11 C‐ER176 TSPO positron emission tomography (PET) was compared to amyloid and tau PET. Correlations between neuroinflammation, amyloid, and tau pathology were examined across disease stages. RESULTS TSPO was upregulated in human microglia under AD‐like inflammatory conditions. Neuroinflammation, defined by PET TSPO, increased in CU A+ participants and became more widespread with increasing disease severity, aligning with worsening amyloid and tau pathology. Associations with tau were particularly extensive in temporal and parietal regions. DISCUSSION These findings suggest probable amyloid association with early microglial activation, while tau pathology is closely tied to wider distribution of neuroinflammation. Highlights TSPO expression increases in human microglia under AD‐like inflammation. 11 C‐ER176 PET shows stage‐dependent neuroinflammation across the AD spectrum. Neuroinflammation overlaps with tau and is more widespread in amyloid‐positive individuals. Activity peaks in MCI, stabilizes later, and relates to vascular and neurodegenerative changes.

Abstract Background The tau PET imaging with 18F‐flortacipir (18F‐FTP) has focused interest in accurately tracking longitudinal tau changes in Alzheimer's disease [1]. Thus, minimizing variability is critical. Off‐target signal has the potential to complicate interpretation of tau‐PET imaging due to potential spillover or bleed‐in effects, with increased effects seen in later stages of Alzheimer's due to cerebral atrophy and proximity of off‐target areas to relevant cortical areas[2]. Previously, we demonstrated the effect of uptake period activity on 18F‐FTP signal. With increasing recent interest in demographic factors that may affect off‐target binding, we investigated the effect of age in extra‐cerebral off‐target signal in 18F‐FTP PET imaging. Method Standard 18F‐FTP PET protocol was performed in 330 participants. Two independent readers measured tau uptake in predetermined off‐target regions, including extraocular muscle, meninges, and occipital bone among others. Linear regression models assessed the relationship between participants’ age, sex, and tau uptake values (SUV and SUVr) (SUV = (Tissue radioactivity concentration / Injected dose) / Body weight & SUVr = SUV of target region/ SUV of reference region.) Result Sex had a significant effect on off‐target signal in most brain regions, with variable positive or negative correlation based on area. Notably, SUV in the eyeball‐retina, occipital bone, upper skull meninges all showed significant positive correlation with sex, with ‘male’ status generally associated with lower SUV. The greatest effect of sex was observed in superior skull meninges, occipital bone, and internal carotid artery ( p <0.001). Age showed minimally affected SUV and SUVr in off‐target regions. SUVr values show a significant correlation with SUV values of upper skull meninges in sex. Cognitive status did not significantly affect off‐target SUV. Conclusion We observed that sex significantly affected extra‐brain off‐target signal in 18F‐FTP PET imaging, while age has minimal effect, adding to previous evidence of off‐target binding within brain regions. Further evaluation is necessary to understand the age‐related effects on off‐target regions. As a result, the variability introduced by sex may pose challenges to studying tau accumulation.

Abstract Background Some data suggest that neuroinflammation may be associated with pro‐inflammatory states in the body. Systemic, proinflammatory cytokines (TNFα, IL‐1, IL‐6, etc.) are produced by a variety of comorbidities. Neuroinflammation has been shown to be associated with the accumulation of amyloid in the brain (Becher, Heneka). We hypothesized that systemic cytokine production could be associated with neuroinflammation and thereby be related to brain amyloid accumulation. This study aims to evaluate the relationship between neuroinflammation, amyloid accumulation and systemic cytokines. Method 90 participants from the Mayo Clinic Study of Aging (MCSA) received a fasting cytokine panel over a 5‐year span where IL‐1, IL‐6, IL‐10, TNFa levels were measured. Participants were divided into four groups: cognitively unimpaired with normal levels of amyloid (CU A‐, n = 25), cognitively unimpaired with elevated amyloid (CU A+, n = 25), and mild cognitively impaired (MCI) with amyloid positive (MCI A+, n = 25), and Alzheimer's disease (AD) with elevated amyloid (AD A+, n = 15). To compare regional standardized uptake value ratio (SUVr) from neuroinflammation PET based on amyloid status (A+ vs A‐) and the four cytokines, Spearman's rank correlation and Fishers r‐to‐z transformations were used to assess pairwise group differences. Result Using Spearman's rank correlation and Fishers r‐to‐z transformations, no strong relationship was found between systemic inflammation and neuroinflammation. While comparing CUA‐ vs CUA+ in Figure 1, three regions (supplementary motor area, frontal superior medial lobe, and middle cingulate cortex) showed a significant difference ( p < 0.05). However, Figure 2 showed an overarching trend bias towards significance across most regions in IL10, IL6, and TNFa. Conclusion Although significant relationships between systemic inflammation, brain amyloid and neuroinflammation were limited, the data show an overarching trend bias in broad brain regions, suggesting that further analyses are needed, possibly with larger sample sizes, to assess the relationship of regional neuroinflammation and amyloid to systemic inflammation. Future analyses could also regress out factors like sex, age, and genotype to further proinflammatory cytokine research.

AbstractBackgroundPlasma Aβ42/40 and p‐tau217 can predict amyloid positivity in cross‐sectional studies. However, it is unclear how plasma biomarkers perform longitudinally, which is important to inform their utility in tracking disease progression. The goal of this study is to describe temporal evolutions of plasma Aβ42/40 and ptau217 ratio (p‐tau217r) measured via mass spectrometry, p‐tau217 measured via an immunoassay, and amyloid PET.MethodsWe included participants in the Mayo Clinic Study of Aging with amyloid PET (PiB) and plasma Aβ42/40 + ptau217r (mass spectrometry, C2N) and/or p‐tau217 (Lilly, MSD). We fit a joint non‐linear mixed effects model with Aβ42/40, p‐tau217r, p‐tau217 (Lilly), and amyloid PET as outcomes and age, sex, APOE ε4 carriership and education as covariates. The model included a per‐participant adjustment (time‐shift) for each biomarker indicating how much earlier or later in time the biomarker was estimated to progress relative to the average participant, a correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing.ResultsThe study included 1459 participants, age 77 (10) years, 44% female, 82% cognitively unimpaired. Mean (SD) years of followup was 5.6(3.0) years for mass spectrometry plasma measures, 2.5(0.7) years for p‐tau217 (Lilly), and 4.6(3.3) for amyloid PET. Model fits are shown for each biomarker, with greater variability in serial measurements for plasma than PET (Figure 1). Individual adjustments between each of the plasma biomarkers and amyloid PET were highly correlated: p‐tau217r, R=0.73 (95%CI: 0.67, 0.78); p‐tau217 (Lilly), R=0.66 (0.62, 0.7); Aβ42/40, R=0.6 (0.52, 0.68) (Figure 2). The p‐tau217r and Aβ42/40 individual adjustments were modestly correlated, R=0.39 (0.28, 0.49). APOE ε4 carriership had the strongest covariate effect on all biomarkers, e.g. p‐tau217r progression 9.4 (6.7, 12) years earlier (Figure 3).ConclusionsPlasma Aβ42/40 and p‐tau217 measured with currently available assays may both be amenable to longitudinal modeling, although the degree of variability in Aβ42/40 over time may pose challenges. Findings suggest that both plasma Aβ42/40 and p‐tau217r may provide information regarding an individual’s timing of progression on amyloid PET; lower correlations with each other than amyloid PET suggests that each analyte may provide complimentary information.

AbstractBackgroundUnderstanding the relationship between AβPET and AβCSF biomarkers will define their potential utility in Aβ treatment. Few longitudinal or neuropathological comparisons have been reported. We assessed the relationship of AβPET and AβCSF biomarkers in a large community cohort.MethodParticipants 50+ years with coincident AβPET and AβCSF biomarkers (pTau181/Aβ42, n=505, and Aβ42/40, n=54) were included. Of these participants, a subgroup was identified that had neuropathology data (n=47) of which many had longitudinal AβPET and CSF testing. The relationships of AβPET and CSF biomarkers were assessed cross‐sectionally in all participants and longitudinally in those with autopsy data. Analyses were performed using AβPET Meta‐ROI and AβPET early‐Aβ‐deposition ROIs (ROIs with early Aβ deposition: fusiform, angular, inferior temporal, middle temporal, middle occipital, and calcarine).ResultIn biomarker tests performed at the same time, more participants were AβPET+ and CSF‐ than AβPET‐ and CSF+ using Meta‐ROI AβPET. This discordance occurred more frequently when using early‐Aβ‐deposition regions (Figure 1). Of 47 participants with autopsy data, 35 had Thal stage ≥1. The sensitivity for earliest detection of cortical Aβ neuropathology by AβPET was 89% (31/35) and by CSF was 66%, (23/35). Eight of these 35 had positive AβPET findings and negative coincident CSF tests at the first time tested. Of these, 5/8 were Meta‐ROI and early AβPET ROI positive. The other 3/8 were only positive on early‐Aβ AβPET ROIs. Four of the 8 with negative CSF and positive AβPET had subsequent testing and 2/4 became positive on CSF tests after 1.5 years while 2/4 continued to be negative on subsequent testing.ConclusionAβPET is more sensitive for detection of early cortical Aβ deposition than CSF biomarkers in cross‐sectional or autopsy data. While the assessment of AβPET by standard Meta‐ROI AβPET methods showed cortical Aβ deposition occurring when not indicated by CSF biomarkers, AβPET ROIs for early Aβ deposition detected additional participants that were AβPET positive and AβCSF negative. These findings have implications for detecting early Aβ neuropathology among CU participants in Aβ prevention trials when robust detection is needed. Further work is needed to compare Aβ plasma biomarkers and AβPET using these methods.

Abstract Aims Excessive alcohol use is associated with an increased risk of cognitive impairment. Since increased amyloid plaque burden exacerbates cognitive decline, we sought to assess the potential impact of alcohol use disorder (AUD) on cognition, memory, and amyloid burden corresponding with age. Methods We conducted the retrospective analysis with 6036 subjects, including 269 AUD+ subjects. A four-item CAGE (C—Cutting Down, A—Annoyance by Criticism, G—Guilty Feeling, E—Eye-openers) alcohol questionnaire was given during the recruitment to determine AUD in each participant. We assessed cognitive function, focusing on memory using neuropsychological testing. For 1038 participants, including 57 AUD+ subjects, we measured amyloid burden using the 11C Pittsburgh Compound B tracer-based positron emission tomography imaging. Results AUD+ was significantly associated with lower scores of cognition and memory function relative to AUD− individuals. No significant association was found with AUD and elevated brain amyloid under the age of 65. However, further analysis showed that those aged ≥65 showed greater odds for abnormal amyloid in AUD+ compared to AUD− participants. Conclusions Our results underscore AUD as a risk factor for cognitive decline and diminished memory, particularly in aging populations. The role of AUD in brain amyloid accumulation requires further study.