Knopman, David S.

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.

AbstractBackgroundAlthough amyloid deposition in the brain is often associated with subsequent dementia risk, not everyone with brain amyloid will develop dementia. This discrepancy illustrates the potential importance that risk factors and lived experiences may have in modifying this association. Compared to participants with low social engagement (SE) in mid‐life, we hypothesized that participants with high mid‐life SE will show a weaker association between amyloid burden and incident dementia.MethodWe included data from 310 non‐demented participants of the Atherosclerosis Risk in Communities (ARIC)‐PET study. Social support and isolation were assessed via interviewer‐administered questionnaires (Visit 2; 1990 – 1992). Based upon categorization of both factors, participants were classified as having high, intermediate, or low mid‐life SE (Table 1). Brain amyloid was evaluated with florbetapir PET (Visit 5; 2011‐2014). Elevated amyloid burden was defined as standardized uptake value ratio (SUVR) >1.2 in the global cortex. Incident dementia cases were identified from visit 5 through 2019 through ongoing surveillance utilizing in‐person neurocognitive testing, informant interviews, and hospitalization codes. Relative contributions of mid‐life SE and elevated florbetapir SUVR to incident dementia, independently and with multiplicative interaction terms, were evaluated with Cox regression models, adjusted for demographics, APOEε4 and vascular risk factorsResultAmong 310 participants, 48 developed dementia (median follow‐up: 4.7 years). Mid‐life SE and elevated SUVR each independently predicted dementia risk but did not interact on a multiplicative scale. Participants with high or intermediate mid‐life SE, relative to low mid‐life SE, were less likely to develop dementia (Table 2). Although the interaction between mid‐life SE and elevated SUVR was not significant, stratified models suggested a stronger association between amyloid burden and dementia in participants with high mid‐life SE (Table 3).ConclusionGreater mid‐life SE was associated with lower odds of developing dementia, independent of elevated SUVR. Although protective, there was no strong evidence for effect modification of mid‐life SE on the association between amyloid and dementia risk. Future longitudinal studies evaluating the potential influence of social factors measured throughout the life course are needed to inform our understanding as to what factors may preserve cognition in the presence of elevated brain pathology.

AbstractBackgroundMemory impairment is generally considered minimal to absent in those with behavioral variant frontotemporal dementia (bvFTD). We sought to assess the frequencies of subjective and objective memory impairment in individuals with mild bvFTD according to consensus criteria.MethodAssessments were performed in mildly symptomatic bvFTD [defined by global FTLD Clinical Dementia Rating scale (CDR®+NACC FTLD) = 1] mutation carriers (Familial) and non‐mutation carriers (Sporadic) who are participants in the ARTFL LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD, www.allftd.org) study. Mutation carriers had a pathogenic mutation in the MAPT, GRN or C9orf72 gene. The presence of subjective memory impairment was defined by a rating >0 on the memory domain of the CDR®+NACC FTLD. Evidence of objective memory impairment was based on performance across 7 memory measures, which include the National Alzheimer’s Coordinating Center Uniform Data Set Version 3 – Craft Story Immediate Recall (CSImm) and Delayed Recall (CSDel), Benson Figure Delayed Recall (BenDel), and the California Verbal Learning Test – Learning (CVLearn), Immediate Recall (CVImm), Delayed Recall (CVDel), and Recognition (CVRecog). Performance was considered abnormal for z‐scores ≤ ‐1.5.ResultData from 38 Familial (mean age 60±8 years, 61% male), and 109 Sporadic (mean age 63±7 years, 70% male) participants were analyzed. Memory ratings >0 on the CDR®+NACC FTLD were common for both Familial (84%) and Sporadic (88%). The frequencies of abnormal performance in the Familial vs Sporadic groups, respectively, per memory measure were: CSImm 43.8% vs 45.9%, p = 0.83; CSDel 46.9% vs 52.6%, p = 0.58; BenDel 29.4% vs 46.4%, p = 0.08; CVLearn 54.8% vs 39.8, p = 0.14; CVImm 64.5% vs 50%, p = 0.16, CVDel 80.6% vs 55.1%, p = 0.01, and CVRecog 51.6 vs 49%, p = 0.80. The frequencies of abnormal performance did not differ (all p>0.05) between Familial and Sporadic for ≥2 tests (79.3% vs 67.7%), ≥4 tests (55.2% vs 44.1%) and ≥6 tests (34.5% vs 31.2%).ConclusionThese data suggest that among those with a mild global severity of bvFTD features: 1) subjective and objective memory impairment are relatively frequent, and 2) the frequency of impairment across memory measures is largely similar between familial and sporadic bvFTD. The presence of memory impairment should not preclude a diagnosis of bvFTD.

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.

AbstractBackgroundThe ALLFTD Consortium aims to characterize preclinical and prodromal stage of familial Frontotemporal Lobar Degeneration (f‐FTLD) to prepare for clinical trials in FTLD.MethodWe prospectively followed 320 asymptomatic participants of the ALLFTD Consortium who are family members of f‐FTLD with a mutation in MAPT (n = 106), GRN (n = 74), or C9orf72 (n = 140), and describe the ratings on the eight domains of CDR® plus NACC FTLD (FTLD‐CDR) in those who phenoconverted to Mild Behavioral and/or Cognitive Impairment (MBCI). MBCI was diagnosed by the proposed research criteria for MBCI‐FTD (Barker MS, et al. Brain 2022). Participants who were diagnosed as MBCI but returned to asymptomatic at the following visit were excluded from the FTLD‐CDR assessment. ANOVA was used to compare continuous variables, and chi‐square test was used to compare categorical variables across the groups.ResultThirty‐six participants (15 MAPT, 9 GRN, 12 C9orf72) developed MBCI during follow‐up visits, 13 participants (5 MAPT, 3 GRN, 5 C9orf72) were diagnosed as MBCI but were characterized as asymptomatic at the following visit, and 271 participants stayed asymptomatic. Percentage of male in MBCI participants was 50% (MAPT 60%, GRN 33%, C9orf72 50%, p = 0.45). Age at phenoconversion to MBCI was 53.6±13.0 years old, the youngest in MAPT and the oldest in GRN participants (MAPT 44.3±8.3, GRN 63.1±11.3, C9orf72 56.4±10.8, p<0.001). Abnormal rating on each of the eight CDR® plus NACC FTLD domains at the initial visit of MBCI phenoconversion were observed in: “Memory” 36% (MAPT 33%, GRN 33%, C9orf72 44%, p = 0.95); “Orientation” 8% (MAPT 7%, GRN 8%, C9orf72 11%, p = 0.93); “Judgment/Problem Solving” 44% (MAPT 47%, GRN 50%, C9orf72 33%, p = 0.73); “Community Affairs” 22% (MAPT 27%, GRN 17%, C9orf72 22%, p = 0.82); “Home/Hobbies” 25% (MAPT 40%, GRN 8%, C9orf72 22%, p = 0.16); “Personal Care” 0%; “Behavior/Comportment/Personality” 44% (MAPT 60%, GRN 42%, C9orf72 22%, p = 0.19); “Language” 31% (MAPT 13%, GRN 50%, C9orf72 33%, p = 0.12).ConclusionThe three most common FTLD‐CDR domains initially disturbed in the prodromal stage of fFTLD were “Behavior/Comportment/Personality”, “Judgment/Problem Solving”, and “Memory”, while “Orientation” was intact in the majority of the individuals. Memory impairment without disorientation was seen in one‐third of the prodromal fFTLD participants.

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.

AbstractBackgroundMost studies on the relationship between neuropsychiatric symptoms (NPS) and cognitive decline focus on NPS data collected over a short period of time. To better understand the association between NPS and cognitive decline, including their temporal relationship, it is important to know NPS status across a person’s life.MethodWe utilized the unique Rochester Epidemiology Project medical record linkage system that has captured medical records as far back as 1907, to abstract the psychiatric history of 1810 cognitively unimpaired participants in the population‐based Mayo Clinic Study of Aging (MCSA). Trained nurse abstractors ascertained the presence of exposure to three common NPS (anxiety, depression, and unspecified emotional distress [UED]), both before and after entry into the MCSA. We conducted Cox Proportional Hazards (PH) models using age as the time scale and adjusting for sex, education, and APOE E4 allele for each NPS in predicting mild cognitive impairment (MCI), or dementia. Additional analyses considering medication use (yes/no) and timing of NPS were also run.ResultAnxiety (Hazard Ratio [HR] = 1.47, p<0.001), depression (HR = 1.36, p = 0.001), and UED (HR = 1.22, p = 0.039) were associated with a higher risk of MCI. These associations were present regardless of medication use for anxiety (With medication: HR = 1.51, p<0.001; without medication: HR = 1.41, p = 0.015), and for those taking medications for depression (With medication: HR = 1.47, p<0.001; without medication: 1.09, p = 0.597). Anxiety was associated with MCI regardless of timing of onset with the strongest relationship being for those with onset of anxiety before 65 that persisted into later life (HR = 1.88, p = 0.004). Depression was associated regardless of timing of onset if the depression was present in late life too. The results were similar for the outcome of dementia (Anxiety: HR = 1.62, p<0.001; depression: HR = 1.31, p = 0.040; UED: HR = 1.37, p = 0.014). The associations accounting for medication use and timing of NPS were similar for the outcome of dementia too.ConclusionNPS is associated with progression to MCI and dementia. Both medication use and timing of NPS might play a role in these associations.

AbstractBackgroundBrief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer’s disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers.MethodParticipants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer’s disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aβ)‐42 and Aβ‐40 to create an Aβ42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers 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, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p’s≤.02), NfL (p’s≤.003), and p‐tau181 (p’s = .04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p’s≤.01) and NfL (p’s≤.03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p’s≤.02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p’s≤.02), and worse Digit Symbol Coding was associated with lower Aβ42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p = .04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p’s>.05).ConclusionRemote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD’s criterion validity.

AbstractBackgroundPatients with dementia with Lewy bodies (DLB) may have Alzheimer´s disease (AD) pathology. Plasma biomarkers of beta‐amyloid (Aβ), phosphorylated tau (pTau), and neurodegeneration are sensitive to AD neuropathologic changes in AD dementia. While these plasma biomarkers are well tested in the AD continuum, their performance for concomitant AD pathology in the DLB continuum is still unclear.MethodWe included patients with isolated REM sleep behavior disorder (iRBD; n = 15), with mild cognitive impairment with Lewy bodies (MCI‐LB; n = 37), and with DLB (n = 70) from the Mayo Clinic Alzheimer’s Disease Research Center. Cognitively unimpaired individuals (CU; n = 100) included from the Mayo Clinic Study of Aging were balanced on age and sex. A panel of plasma biomarkers of Aβ40, Aβ42, p‐tau181, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) measures was obtained. We calculated the ratio of plasma Aβ40/42. Lower Aβ40/42 indicates increased Aβ pathology. Plasma Aβ40, Aβ42, GFAP, and NfL were measured using the Simoa® Neurology 4‐Plex E Advantage kit and pTau‐181 was measured with the Simoa® pTau‐181 Advantage V2 kit. Both were run on a Quanterix HD‐X analyzer. AD imaging biomarkers were assessed on PET using Pittsburgh compound B (PiB) for Aβ and 18F‐Flortaucipir for tau.ResultHigher levels of plasma GFAP were observed in the MCI‐LB (p = 0.014) and DLB (p<0.001) groups compared to CU (Figure). DLB patients also showed higher levels of plasma pTau181 (p<0.001) and NfL (p<0.001). Higher amyloid and tau PET standardized uptake value ratio (SUVr) in the DLB continuum were associated with higher levels of plasma p‐tau181, NfL, and GFAP (p<0.001). After accounting for age, plasma Aβ40/42 did not correlate with amyloid or tau PET SUVr.ConclusionIn the DLB continuum, abnormal levels of GFAP in plasma can be detected as early as the prodromal stages of the disease. Plasma pTau181 reached abnormal levels in the DLB stage. Higher plasma GFAP, NfL, and p‐tau181 are associated with higher amyloid and tau PET SUVr in the DLB continuum. Plasma biomarkers have the potential to contribute to screening and early diagnosis in the DLB continuum, which has implications in designing new treatments.

AbstractBackgroundWe have previously shown that 3D T1‐weighted and 3D T2‐FLAIR brain MRI can potentially be re‐identified using face recognition, but this has not been tested for other MRI sequences commonly used in imaging studies of aging and dementia. Here we compare match rates of automated face recognition to re‐identify 3D T1‐weighted, 3D T2‐FLAIR, 3D T2‐weighted, 2D Axial T2‐FLAIR, multi‐echo GRE (T2*), diffusion MRI, functional MRI, ASL, and T2* GRE images.MethodWe used a dataset of 182 Mayo Clinic participants each with MRI and face photographs. MRIs used Siemens and GE vendor‐product sequences similar to ADNI3 and ADNI4 protocols. We created face reconstructions (“basic”, with minimal preprocessing, and “advanced” that automatically filled missing face regions using a template) from each MRI sequence, and we trained a Microsoft Azure PersonGroup face recognition classifier to recognize these MRI‐based faces. We then queried the classifier with five face photographs for each participant, and we recorded how often the classifier chose the correct MRI as the top‐ranked match. For image types with match rates > = 10%, we then repeated the experiment after de‐facing each MRI with mri_reface, our leading software that replaces faces in each image (“re‐faces”) with an average face.ResultResults are presented in Table 1. 3D T2‐FLAIR, 3D T1‐weighted, and 3D T2‐weighted images were matched at 96‐98%, and mri_reface reduced all to 8%. Axial T2‐FLAIR and ME‐GRE were matched at 32‐45%, and mri_reface reduced both to 1%. T2*‐GRE matched at 10%, and mri_reface reduced this to 1%. ASL matched at 8%, while dMRI and fMRI matched at only 0‐2%.ConclusionAmong current‐generation vendor‐product sequences, we recommend that de‐facing be applied before research data sharing of T1‐weighted, T2‐weighted, T2‐FLAIR, T2*, and ME‐GRE images. Minimal match rates for current‐generation product fMRI, ASL, and fMRI images suggest that they present low risk of re‐identification and can be shared without de‐facing, but this conclusion should be re‐evaluated if they are acquired without fat‐suppression, with a larger field of view, or if newer developments reduce the current levels of artifacts and distortion around the face.