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AbstractBackgroundThe mapping between risk factors and biomarkers with cognitive performance and decline in the older adults is complex and varies significantly across different age and sex groups. Our goals were to (i) develop a machine learning (ML) model to predict baseline cognition and cognitive decline over a 5‐year follow‐up using only baseline assessments, and (ii) identify important modifiable and non‐modifiable predictors of cognitive decline across different age strata.MethodWe utilized longitudinal data from 2219 participants (mean age (SD): 70 (12) years, 49% male) from Mayo Clinic Study of Aging who had 43 features belonging to cardiovascular health, plasma, MRI (diffusion and structural), and lifestyle measures. Cognition was measured using a global cognition z‐score. We built ML models to predict baseline cognition and rate of cognitive decline over 5‐years (linear regression slopes over all individual followup measurements for each participant) using baseline assessments as inputs. We then built a model for each category separately and as a pooled model with all measures included, incorporating education and sex as demographic features in all models (Fig. 1). We applied a multi‐layer stack ensemble ML technique that utilized repeated k‐fold bagging to maximize accuracy. Importance of the top predictors were assessed in three age strata (55‐65 years (31%), 65‐80 years (46%), 80‐95 years (23%)).ResultThe pooled model explained the largest variance for prediction of baseline cognition and cognitive decline (Fig. 2). In individuals ≤80 years, lower NfL was most predictive of higher baseline cognition. A combination of imaging and plasma features predicted the 5‐year cognitive decline. Additionally, in individuals ≤65 years, diabetes and hypertension were important predictors of faster cognitive decline and in those >80, lower BMI and midlife physical/cognitive activity was an important predictor of faster cognitive decline (Fig. 3).ConclusionThe plasma model was best for baseline cognition prediction and imaging model was best for cognitive decline prediction. There was significant heterogeneity in important features across the age strata. In individuals >65 years, plasma GFAP was the most important predictor of cognitive decline. Our model allowed identification of key risk factors that predicted cognitive decline.

AbstractBackgroundThe implications of a positive tau (T) Positron Emission Tomography (PET) with a negative beta amyloid (A) PET are not well understood. We investigated cognitive performance in participants who were positive on tau PET but negative on beta amyloid PET.MethodWe evaluated 98 participants from the Mayo Clinic Study of Aging (MCSA) and the Mayo Clinic Alzheimer’s Disease Research Center (ADRC) who were positive for Flortaucipir (FTP) and negative for PiB PET. Participants were classified as A‐ and T+ based on Standardized Uptake Value Ratios (SUVr) of FTP and PiB PET scans, and were matched 2:1 to amyloid negative and tau negative cognitively unimpaired controls from the MCSA. Cognitive test scores were compared between the combined ADRC/MCSA group and MCSA only group. We also examined clinical diagnoses in our cohort. The most common were Alzheimer’s Disease, mild cognitive impairment (MCI), Dementia with Lewy Bodies (DLB) and Frontotemporal dementia (FTD) amongst others.ResultThere were no significant differences in demographic features or PiB SUVr between groups. There were significant differences in APOE genotype between groups. In the combined ADRC/MCSA group, 25% of A‐T‐ and 13% of A‐T+ were APOE e4 carriers (p = 0.016), and in the MCSA only group, 27% of A‐T‐and 10% of A‐T+ were APOE e4 carriers (p = 0.010). In the combined ADRC/MCSA group, the A‐T+ group demonstrated lower performance than the A‐T‐ group on the Mini Mental Status Exam (MMSE) (p<0.001), Wechsler Memory Scale‐Revised Logical Memory I (p<0.001) and Logical Memory II (p<0.001), Auditory verbal Learning Test Delayed Recall (AVLT) (p = 0.004), Category fluency animals (p = 0.005), Category fluency vegetables (p = 0.021), Trailmaking test A (p<0.001), Trail making test B (p<0.001), Wechsler Adult Intelligence Scale‐IV (WAIS‐IV) (p = 0.002), and Unified Parkinson’s Disease Rating Scale part III (UPDRS III) (p = 0.003). In the MCSA only group, the A‐T+ group had lower scores on Logical Memory II (p = 0.022), AVLT delated recall (p = 0.04), and WAIS‐R block design (p = 0.035) compared to the A‐T‐group.ConclusionPositive tau PET in the absence of beta‐amyloid positivity is associated with lower cognitive performance.

AbstractBackgroundThe balance between production, clearance, and toxicity of Ab peptides is central to AD pathobiology. Though highly variable in terms of age of symptom onset (AAO), hundreds of pathogenic variants in Presenilin‐1 (PSEN1) cause autosomal dominant forms of AD (ADAD). PSEN1 forms the catalytic core of the γ‐secretase complex and thereby directly mediates the production of longer, aggregation‐prone Aβ peptides relative to shorter, non‐aggregating peptides. We hypothesized that the broad AAO and biomarker heterogeneity seen across ADAD would be predictable based on mutation‐specific differences in γ‐secretase function, as measured by a ratio of production of shorter vs. longer Aβ species.MethodsAβ‐37, 38, 40, 42, and 43 production was quantified from 162 unique PSEN1 variants expressed in HEK293 cells engineered to lack endogenous wild‐type PSEN1/2 (Figure 1). Prediction of AAO was carried out in 107 PSEN1 variants (characterized by Liu et al, 2022) and then replicated with a set 55 unique PSEN1 variants represented in the Dominantly Inherited Alzheimer Network (DIAN; n = 190 corresponding variant carriers with detailed cognitive and biomarker data; Figure 2).ResultsMutation‐level variations in Aβ production, including a novel composite representing γ‐secretase function (lower score = less g‐processivity), from the cellular model were highly predictive of actual AAO across the 162 mutants examined (Non‐DIAN variants [N = 107]: r = 0.71, p <2.2e‐16; DIAN variants [N = 55]: r = 0.61, p = 6.10e‐07). Our cell‐based γ‐secretase function composite was strongly associated with cerebral PiB‐PET β‐amyloid burden (B[SE] = ‐0.03[0.01], p = 4.06e‐07), MRI gray matter volume (B[SE] = 44.22[6.4], p = 5.15e‐01), cerebrospinal fluid Aβ42/40 (B[SE] = 6.3e‐04[1.5e‐04], p = 4.46e‐05) and phosphorylated tau‐217 (B[SE] = ‐0.01[0.002], p = 1.98e‐05), Clinical Dementia Rating®‐ Sum of Boxes (B[SE] = ‐0.07[0.02], p = 4.86e‐05), and Mini‐Mental State Exam (B[SE] = 0.11[.03], p = 2.17e‐04).ConclusionsBiochemical variations in γ‐secretase function across PSEN1 pathogenic variants broadly predicted the cross‐sectional clinical, cognitive, and biomarker course of ADAD, including AAO. These findings elucidate the critical link between γ‐secretase function, Aβ production, and severity of AD. The novel approach designed here also represents a tool to account for heterogeneity in ADAD clinical trials and to assess the pathogenicity of PSEN1 variants of unknown significance or with limited family history.

AbstractBackgroundBoth 11C‐Pittsburgh compound‐B (PiB) and 18F‐Flutemetamol (FMT) have off‐target white matter (WM) signal that increases with age, but WM uptake is stronger with FMT than PiB. Although their WM patterns are comparably associated with age, the difference between them may impact visual and quantitative assessments of cortical amyloid‐β uptake. Our objective was to determine whether PiB PET and FMT PET visual assessments agree with quantitative cortical amyloid‐β evaluations.MethodA cohort of cognitively unimpaired (CU) younger adults with median (range) of 39 (30, 48) years (N = 30), CU older adults with age of 67 (61, 83) years (N = 30) and older adults with AD dementia with age of 67 (54‐84) years (N = 23) underwent MRI, PiB PET and FMT PET. PiB‐PET and FMT PET scans were evaluated visually by two nuclear medicine specialists blinded to participant information. Disagreements were reconciled with a second, joint round of visual reads. Readers applied standard visual assessment criteria using the absence of regional gray matter (GM) and WM contrast as an indication of a positive scan. The global cortical PiB standard uptake value ratio (SUVr) was obtained referencing to cerebellar crus uptake and converted to centiloid values. Participants were classified as PiB positive/negative using a centiloid cut‐off of 22.ResultPiB and FMT visual positivity agreed with quantitative positivity on PiB‐based centiloid values in 69/83 for PiB and 78/83 for FMT. There were 10 disagreements with PiB, 1 disagreement with FMT and 4 disagreements with both. Few disagreements occurred near the centiloid threshold. Interestingly, all disagreements between visual reads (amyloid‐β positive) and centiloid (amyloid‐β negative) in the CU younger were with PiB. FMT disagreements were always in the CU older and AD groups.ConclusionDisagreements between amyloid‐β positivity on visual reads and quantitative measurements (centiloid) may depend on age and tracer. PiB WM uptake is lower than FMT WM uptake, which may lead to reduced contrast between GM and WM (part of the standard criteria for visual reads), especially in younger ages. In contrast, WM uptake increases at older ages, and increased WM signal may bleed more into cortical regions, hiding subtle, near‐threshold cortical uptake, especially with FMT.

AbstractBackgroundHearing loss is associated with cognitive decline and dementia. The ongoing ACHIEVE Study was the first randomized trial to test the effects of hearing treatment on three‐year cognitive change. The ACHIEVE – Brain Health Follow‐Up Study will extend follow‐up to six years post‐randomization to determine the long‐term and potentially nonlinear effects of hearing treatment vs. health education control with delayed treatment on dementia and cognitive decline.MethodsParticipants will be recruited from the 977 participants enrolled in the ACHIEVE Study. After Year 3, hearing intervention participants will continue to receive hearing care and the control group will be offered the hearing intervention. Co‐primary study outcomes will be cognitive decline and a composite of incident mild cognitive impairment (MCI)/probable dementia. Secondary outcomes include domain‐specific cognitive decline, brain structural changes on magnetic resonance imaging (MRI), and other functional outcomes (e.g., physical activity, social isolation). Power analyses for the co‐primary outcomes assumed an annual attrition rate of 10% and an immediate, lagged, or cumulative treatment effect.ResultsWhen comparing multi‐year cognitive decline (Table 1), in the scenario of a constant treatment effect that assumes a strong and immediate treatment effect, the benefit of extended follow‐up is not greater power, but additional information on the long‐term effects of hearing intervention. In a scenario of a lagged treatment effect, a two‐year lag in the treatment effect causes the Year 3 assessment to be severely underpowered (28%) but provides sufficient power (80%) at Year 6 despite attrition. In a scenario of a cumulative treatment effect that increases with time, analyses are similarly underpowered at Year 3 (65%) but not at Year 6 (82%). The same pattern is evident when estimating the power at Year 3 (57%) and Year 6 (80%) to detect incident MCI/probable dementia in a Cox regression model that assumes a protective hazard ratio of 0.70 (Figure 1).ConclusionAcross all non‐constant scenarios of potential treatment effect, six years of follow‐up increases precision and provides valuable information to better delineate long‐term cognitive trajectories. The ACHIEVE ‐ Brain Health Follow‐Up Study will be completed in 2025.

AbstractBackgroundHigher blood pressure (BP) is associated with greater white matter hyperintensity (WMH) volume, but research is limited in recently menopausal and postmenopausal women. Postmenopausal women have a greater burden of WMH than men and premenopausal women, but whether hormone therapy (HT) modifies the association between systolic and diastolic BP (SBP, DBP) and WMH is unknown. We investigated the association of SBP and DBP in recently postmenopausal women who participated in an HT trial with WMH volumes measured 14 years later and explored whether HT modified the results.MethodWomen with good cardiovascular (CV) health, who were previously enrolled in a randomized placebo‐controlled HT trial, the Kronos Early Estrogen Prevention Study (KEEPS), participated in the current observational KEEPS continuation (n = 212; median age = 67; range 58‐72). SBP and DBP were measured at KEEPS baseline (median age = 54 years; range 44‐59) and continuation. Antihypertensive users were excluded (baseline, n = 34; continuation, n = 61). Approximately 10 years after the end of KEEPS HT versus placebo 4‐year interventions, automated WMH volumes were measured from 3D‐FLAIR MRI. Associations of KEEPS baseline and continuation SBP and DBP with log WMH volume at KEEPS continuation were tested using linear regression analyses adjusting for covariates in model 1 (age, total intracranial volume, study site) and model 2 (model 1 plus cardiovascular risk factors (CVRF)).ResultHigher KEEPS baseline SBP and DBP were associated with greater WMH volume measured 14 years later, and after adjusting for CVRF (p≤0.01). Similarly, in KEEPS continuation, higher SBP and DBP were associated with greater WMH volume measured concurrently and after adjusting for CVRF (p<0.001). We did not find statistically significant evidence that HT versus placebo modified these associations. Topographically, higher SBP and DBP were associated with greater periventricular WMH in the frontal and parietal lobes after adjusting for age (Figure).ConclusionHigher SBP and DBP in recently menopausal women with good CV health were associated with greater WMH volumes more than a decade later after adjusting for CVRF, independent of randomization to HT. Our results show the importance of blood pressure control in recently menopausal women, that may reduce the white matter injury later in life.

AbstractBackgroundThere has been a rise in remote cognitive testing for neurodegenerative diseases. The ALLFTD Mobile App is designed to capture clinical features of frontotemporal dementia (FTD) through repeated remote assessment on smartphones. This is a departure from traditional neuropsychological practice in which a single examiner‐led assessment occurs in a controlled setting. When participants complete tasks on their own, those with greater disease severity may be more variable in their performance for a variety of reasons (e.g. greater distractibility, less engagement). Here we study whether intraindividual cognitive variability (IIV) is associated with older age and increasing disease severity.MethodParticipants were 279 (mean age = 54.9) healthy controls and participants with sporadic FTD or from familial FTD kindreds with a range of disease severities based on FTLD Clinical Dementia Rating scale (CDR®+NACC‐FTLD Global) scores: 0 (n = 132), 0.5 (n = 54), and ≥1 (n = 47). Participants completed three cognitive tasks at least two times across twelve days. Measures included an adaptive memory task and gamified versions of Flanker and Go/no‐go tasks. IIV was operationalized as the standard deviation in performance on each task. Regression analyses investigated the relationship of IIV with CDR®+NACC‐FTLD group, and with age in those without symptoms. Education and gender were included as covariates in all analyses.ResultGreater disease severity (CDR®+NACC‐FTLD) was associated with greater variability on Flanker (p<.001) and Go/No‐go (p = .02) tasks; a positive but non‐significant association was seen with memory testing (p = .16). Post‐hoc comparisons found that those with CDR®+NACC‐FTLD of 0.5 had significantly more IIV on the Flanker task (𝛃 = .39, p = .004) than those with CDR®+NACC‐FTLD’s of 0. Moreover, those with a CDR®+NACC‐FTLD ≥1 had significantly greater IIV on the Flanker (𝛃 = .85, p<.001) and Go/No‐go tasks (𝛃 = .51, p = .007) than those with a CDR®+NACC‐FTLD of 0. In those without symptoms (CDR®+NACC‐FTLD = 0), age was associated with increased IIV on the Flanker (𝛃 = .27, p<.001) and Go/no‐go (𝛃 = .23, p<.001) tasks.ConclusionIncreased IIV, particularly on processing speed/executive functioning tasks, was associated with greater age and greater functional impairment, even in the mildest stages. Future investigations exploring predictors of IIV and the prognostic value of IIV are warranted.

AbstractBackgroundFrontotemporal dementia (FTD) is a leading cause of dementia in individuals under age 65. Due to limitations in access to in‐person evaluations, there is an urgent need to examine remote, accessible, and low‐burden assessment techniques for FTD. Unobtrusive monitoring of at‐home computer use in older adults has shown that those with mild cognitive impairment (MCI) have significant declines in computer use over time. To extend these findings, we characterized daily trajectories in smartphone battery percentage, a proxy of smartphone use, and examined relationships with clinical indicators of disease severity in FTD.MethodParticipants included 250 adults enrolled in the ALLFTD Mobile App study (mean age = 53.9 [SD = 14.9]; 59% women; 94% White). To assess clinical disease severity, all participants completed the FTLD Clinical Dementia Rating scale (CDR®+NACC FTLD), a brief neuropsychological battery, the Neuropsychiatric Inventory Questionnaire (NPI‐Q), and brain MRI. The ALLFTD Mobile App was installed onto participants’ smartphones for remote, passive, continuous monitoring of smartphone use (average days of monitoring = 82 [range = 7‐541]). Battery percentage was collected every 15 minutes. Linear mixed effects models examined linear, quadratic, and cubic effects of time of day (i.e., hour; modeled as person‐specific random effects) and their interactions with each measure of disease severity on battery percentage. Models covaried for age.Result57% of participants were clinically normal, 21% had MCI, and 22% had dementia. CDR®+NACC FTLD Global score interacted with hour such that participants with MCI and dementia had flatter battery curves (i.e., less change in battery percentage throughout the day – a proxy of less smartphone use) than clinically normal participants (ps<0.001). Additional models showed that worse memory (hour3 x memory score: 𝛃 = 0.59, p<0.001) and executive functioning (hour3 x executive score: 𝛃 = 0.64, p<0.001), more neuropsychiatric symptoms (hour3 x NPI‐Q: 𝛃 = ‐0.22, p = 0.004), and smaller gray matter volumes (controlling for intracranial volume; hour3 x volume: 𝛃 = 0.49, p<0.001) were also statistically significantly related to less battery usage throughout the day.ConclusionThese findings are proof‐of‐concept that passively acquired smartphone data, specifically battery usage, can provide insights about disease progression in FTD. Results support future research focusing on the development of passively collected digital metrics sensitive to clinical declines in neurodegenerative disease.

AbstractBackgroundQuantified accelerated aging, or “age gap,” defined as the difference between a person’s estimated age using a biological measurement, such as a brain image, and their true age, has strong associations with multiple neurodegenerative etiologies. As an alternative to imaging‐based measurements, in this study we developed an age prediction model based on gait patterns and used this model to quantify accelerated aging as an indicator of cognitive impairment and predictor of future cognitive decline.MethodA database of 16,917 pressure sensitive walkway exams from 5,307 participants in the Mayo Clinic Study of Aging was used to develop an age estimation model that included two main components. First, a novel dimensionality reduction and feature selection algorithm was used to extract gait features. Second, a regularized linear model was trained using these features to produce a sparse, clinically interpretable estimator. This model was used to calculate a gait age gap metric, which was tested for association with measures of cognitive impairment and future cognitive decline.ResultThe distributions of gait age gap in age‐matched participants defined as having no cognitive impairment (CU, n = 573), mild cognitive impairment (MCI, n = 520), or a clinical diagnosis of dementia due to Alzheimer’s (AD, n = 53) are shown in Fig 1a. A significant shift in the mean is observed between all three groups. Fig 1b. shows the distributions of baseline gait age gap values amongst individuals categorized by cognitive decline: stable CU (n = 644), MCI converters (n = 89), stable MCI (n = 78), and significant impairment converters (SI, n = 35). The baseline gait age gap in stable CU individuals was found to be significantly lower than in the already impaired or converter groups.ConclusionIn this study, we developed a model for estimating accelerated aging from gait variables in the form of a gait age gap metric. This metric was shown to be strongly associated with AD‐driven cognitive impairment as well as predictive of future cognitive decline. These results highlight the potential clinical utility of quantitative gait analysis in behavioral neurologic assessment of cognitive impairment in AD.

AbstractBackgroundEasily accessible and self‐administered cognitive assessments that can aide early identification of individuals at risk for Alzheimer’s disease (AD) dementia are critical for timely intervention. Mayo Test Drive (MTD) is a remote, self‐administered, multi‐device compatible, web‐based cognitive assessment (Stricker et al., 2022). We investigated continuous associations between MTD and amyloid and tau PET biomarkers and compared the strength of association to PET biomarkers and in‐person cognitive measures.Method556 adults from the Mayo Clinic Study of Aging participated (mean age = 70.6; mean education = 16.0 years; 50.7% female; 97.5% white). Participants were predominantly cognitively unimpaired (95.3%; 4.7% had a consensus diagnosis of mild cognitive impairment). Participants completed (1) brain amyloid and tau PET scans, (2) in‐person neuropsychological assessment including Mayo Preclinical Alzheimer’s disease Cognitive Composite (Mayo‐PACC) and Global‐z, and (3) remote MTD comprised of the Stricker Learning Span (SLS) and the Symbols test, which are combined into an MTD composite. Multiple regressions adjusted for age, sex, and education queried associations between imaging biomarkers [amyloid meta‐region of interest (ROI), tau meta‐ROI, and tau entorhinal cortex (EC)‐ROI)] and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites.ResultLower performances on MTD composite and Mayo‐PACC were associated with higher amyloid meta‐ROI (p’s<.05; Table 1). MTD composite was also negatively associated with tau meta‐ROI and EC‐ROI (p’s<.05). For memory measures, SLS sum of trials was negatively associated with tau meta‐ROI (p = .01) and EC‐ROI (p<.001) with no associations between AVLT sum of trials and PET (p’s≥.06; Table 2). Among processing speed/executive function measures, worse Trails B performance was associated with higher amyloid meta‐ROI (p<.001). Symbols and Digit Symbol Coding were not associated with PET (p’s≥.29). A multivariable model with MTD explained significantly less variance (p = .04) for amyloid meta‐ROI (adjusted R‐squared = 0.165) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.176); remote and in‐person composite measures were comparable for other associations (p’s>.05).ConclusionMTD is associated with continuous measures of amyloid and tau PET biomarkers of AD, demonstrating criterion validity for MTD. Several associations were comparable to relationships between gold‐standard in‐person neuropsychological tests and imaging biomarkers.