Syrjanen, Jeremy A.

AbstractBackgroundHigher brain amyloid PET is associated with slower gait speed and has been hypothesized to play a role in age‐related mobility decline. With the development of blood‐based biomarkers of Alzheimer’s disease pathology, we examined whether plasma levels of amyloid‐beta (Aβ)40, Aβ42, the Aβ42/Aβ40 ratio, phosphorylated tau 181 (p‐tau181) and 217 (p‐tau217), total tau, and neurofilament light chain (NfL) were associated with gait speed.MethodThis study included 779 cognitively unimpaired participants (median age 75, 54.8% male, median follow‐up 6.8 years) in the Mayo Clinic Study of Aging with concurrent measurement of plasma biomarkers and gait speed. Individuals with chronic kidney disease or a history of stroke were excluded. The plasma markers (pg/mL) were measured on the Quanterix Simoa® HD‐1 analyzer (total tau, Aβ42, Aβ40, NfL) and the Meso Scale Discovery platform (p‐tau181, p‐tau217). Gait speed (m/s) was obtained by timing a self‐paced walk across 25 ft using a stopwatch. Linear mixed effects models, with subject‐specific intercepts and slopes for time since baseline, with z‐scored baseline plasma markers were used to predict cross‐sectional and longitudinal gait speed. All models adjusted for baseline age and sex.ResultAt baseline, higher Aβ42 [Beta = ‐0.019; 95%CI = (‐0.035, ‐0.004)], total‐tau [‐0.026; (‐0.041, ‐0.011)], and p‐tau181 [‐0.021; (‐0.036, ‐0.006)] were associated with slower gait speed. Gait speed decreased, on average, by about 0.015 m/s annually for those in this study. Each one standard deviation (SD) increase in baseline level of plasma Aβ40 [‐0.005; (‐0.007, ‐0.002)], NfL [‐0.006; (‐0.009, ‐0.004)], and p‐tau217 [‐0.004; (‐0.007, ‐0.001)] accelerated this decline over time. In contrast, each one SD increase in Aβ42/Aβ40 ratio [0.003; (0.001, 0.006)] mitigated decline. Additional analyses adjusting for Charlson comorbidity index, Beck Depression Inventory, body mass index, and APOE E4 allele status gave similar results.ConclusionBoth cross‐sectional and longitudinal associations of plasma markers of amyloid and tau pathology and neurodegeneration with gait speed among cognitively unimpaired individuals were present. Therefore, not only are these markers of cognitive changes, they are also related to motor changes such as those affecting gait speed.

There is a growing interest in the application of machine learning (ML) in Alzheimer’s disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.

AbstractBackgroundNeurofilament light chain (NfL) is an emerging blood‐based biomarker useful for assessing neuro‐axonal injury in several neurological conditions. Plasma NfL concentrations reflect the extent of axonal damage, making it a general marker of neurodegenerative disease progression. This study evaluates the analytical performance characteristics of the Quanterix NfL Advantage method in EDTA‐plasma.MethodThe assay was performed per manufacturer’s instructions on the Quanterix Simoa HD‐X Analyzer. Analytical validation included analyte stability, imprecision, lower limit of quantification (LLOQ), analytical measurement range (AMR), accuracy, and interferences. One‐sided reference intervals were established using quantile regression methods among 1100 cognitive normal individuals, age 20 to 95 years without known chronic kidney disease, stroke, or myocardial infarction.ResultNfL was stable in plasma three days ambient, seven days refrigerate, 90 days frozen (‐20C), and over 3 freeze/thaw cycles. Intra and inter‐assay imprecision, expressed as % coefficient of variation (%CV), ranged from 3.4 – 6.0% and 5.9 – 11.6% respectively. The LLOQ was evaluated using patient plasma pools at concentrations of 1.7 and 3.6 pg/mL. %CVs were 16.8% and 9.7% respectively, and the LLOQ was set at 3.0 pg/mL. Accuracy was assessed by spiking 5 plasma samples with NfL calibratoran 87% average recovery (range 81‐94%) was observed. The AMR of 3.0 – 1500 pg/mL was verified with a regression slope of 0.93 and r2 > 0.99. Additionally, plasma admixtures covering the AMR showed acceptable linear response (slope of 1.02 and r² >0.99). The assay was not affected by icterus to 100 mg/dL, lipemia to 1000 mg/dL, hemolysis to 1000 mg/dL at NfL concentrations >30 pg/mL or to 200 mg/dL at NfL concentrations ≤30 pg/mL. NfL increased with age but did not differ by sex. The data supported age‐based reference intervals broken down into 10‐year intervals. The 97.5th percentile limits were: 20s: ≤8.4 pg/mL, 30s: ≤11.4 pg/mL, 40s: ≤15.4 pg/mL, 50s: ≤20.8 pg/mL, 60s: ≤28.0 pg/mL, 70s: ≤37.9 pg/mL, 80+: ≤51.2 pg/mL.ConclusionThe analytical performance characteristics of the plasma Quanterix NfL advantage assay were deemed acceptable for clinical laboratory implementation. The establishment of NfL reference intervals will aid in the clinical interpretation of patient plasma NfL concentrations.

AbstractBackgroundPhysical and cognitive activities are associated with decreased risk of mild cognitive impairment and dementia. However, the longitudinal associations between physical and cognitive activities with trajectories of Alzheimer’s disease (AD) neuroimaging biomarkers among older adults free of dementia remain unclear.MethodWe conducted a longitudinal study derived from the population‐based Mayo Clinic Study of Aging, including individuals aged ≥ 50 years who were free of dementia. Participants had information on physical and cognitive activity engagement in midlife (ages 30‐45 for participants aged 50‐69; ages 50‐65 for participants aged ≥ 70) and late‐life (12 months prior to baseline assessment), and AD biomarker assessments. We calculated linear mixed‐effect models to examine the association between baseline physical and cognitive activity composite scores for mid‐ and late‐life, and trajectories for individual yearly change in amyloid deposition (measured by PiB‐PET), tau burden (measured by Tau‐PET), regional glucose hypometabolism (measured by FDG‐PET), cortical thickness (measured by MRI), and white matter hyper intensities (WMH, measured by FLAIR‐MRI). The models were adjusted for age, sex, and APOE ɛ4 carrier status.ResultThe sample included 2755 persons (52% males; 2462 cognitively unimpaired, 293 with MCI). The mean [SD] age was 72.7 [9.8] years; 774 participants were APOE ɛ4 carriers. Although, overall, participants showed an increase in amyloid deposition, tau burden, WMH, and a decrease in glucose metabolism and cortical thickness over time, those with higher late‐life physical activity experienced less pronounced increase in Tau‐PET SUVR over time [estimate for late‐life physical activity with time interaction: ‐0.002; 95% CI ‐0.004, ‐0.000; p = 0.034]. In addition, participants with higher cognitive activities in midlife [est. for midlife cognitive activity with time interaction: 0.001; 95% CI 0.000, 0.003; p = 0.026] and late‐life [est. for late‐life cognitive activity with time interaction 0.002; 95% CI 0.001, 0.003; p = 0.005] had less pronounced decrease in FDG‐PET SUVR over time. There were no further significant interactions.ConclusionPreliminary findings suggest significant associations between 1) late‐life physical activity and less tauopathy, and 2) cognitive activities in mid‐ and late‐life with less synaptic dysfunction over time. Further research is needed to validate study findings.

AbstractBackgroundThe three most common genes responsible for the familial Frontotemporal Lobar Degeneration (f‐FTLD) are MAPT, GRN, and C9orf72, with the C9orf72 mutation being the most common cause of f‐FTLD. The ALLFTD Consortium targets to characterize f‐FTLD, including its preclinical stage, to prepare for clinical trials in FTLD. We sought to investigate the initial symptoms and clinical picture of f‐FTLD and compare them among the common pathogenic genes using longitudinal CDR® plus NACC FTLD.MethodWe longitudinally assessed the CDR® plus NACC FTLD among 292 asymptomatic participants of the ALLFTD Consortium who are family members of f‐FTLD caused by MAPT (n = 99), GRN (n = 67), or C9orf72 (n = 126) mutations, and described the detailed profiles of the CDR® plus NACC FTLD in the participants who developed Mild Behavioral and/or Cognitive Impairment (MBCI).ResultForty‐one participants (17 MAPT, 11 GRN, 13 C9orf72) developed MBCI during follow‐up visits . Age at the diagnosis of MBCI was 52.3±13.0 years (GRN 63.2±11.8, MAPT 43.5±8.6, C9orf72 54.7±10.7). Frequencies of abnormal rating on the eight CDR® plus NACC FTLD domains at the time of MBCI diagnosis were: “Memory” 44% (MAPT 47%, GRN 55%, C9orf72 31%); “Orientation” 7% (MAPT 6%, GRN 9%, C9orf72 8%); “Judgment and Problem Solving” 39% (MAPT 41%, GRN 55%, C9orf72 23%); “Community Affairs” 22% (MAPT 24%, GRN 18%, C9orf72 23%); “Home and Hobbies” 22% (MAPT 35%, GRN 0%, C9orf72 23%); “Personal Care” 0%; “Behavior/Comportment/Personality” 51% (MAPT 59%, GRN 55%, C9orf72 38%); “Language” 29% (MAPT 18%, GRN 55%, C9orf72 23%).ConclusionInitial symptoms and clinical picture of f‐FTLD in MBCI stage differ among responsible FTLD genes, which could be useful in detecting very early features of f‐FTLD in pathogenic mutation carriers.

AbstractBackgroundSlow walking speed is associated with numerous negative health outcomes including global cognitive decline.1 More detailed gait analysis can differentiate among different dementia subtypes,10 but the predictive power of baseline gait metrics for domain‐specific cognitive decline needs further characterization.MethodWe conducted a follow up study on 534 participants of the Mayo Clinic Study of Aging who underwent evaluations every 15 months (median follow‐up = 5.4 years). We analyzed quantitative gait metrics, acquired with a pressure‐sensing walkway (GAITRite® instrument), and cognitive scores using a linear mixed effects model to identify correlations between z‐scored baseline gait metrics (cadence, swing time, stance time, velocity, double support time (DST), stride length, stride length standard deviation (SD), stride time SD, swing time SD, stance time SD, DST SD) and cognitive domain z‐scores (memory, attention, language, visuospatial, global) at baseline and over time. Models controlled for age, sex, and probable REM sleep behavior disorder at baseline.ResultThe table includes both baseline associations and difference in slopes (Δ), relative to the average person, in cognitive domains. These slopes ranged from about ‐0.05 for visuospatial to ‐0.12 for attention.ConclusionIncreased gait velocity and stride length are associated with less severe domain‐specific and global cognitive decline while increased swing time SD and DST are associated with more severe rates of decline in some domains. These findings suggest that baseline gait characteristics could be a helpful predictor of future cognitive changes.References:1. Savica R, Wennberg AMV, Hagen C, et al. Comparison of Gait Parameters for Predicting Cognitive Decline: The Mayo Clinic Study of Aging. Journal of Alzheimer's disease : JAD. 2017;55(2):559. doi:10.3233/JAD‐1606972. Mc Ardle R, del Din S, Galna B, Thomas A, Rochester L. Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors? Gait & Posture. 2020;76:372–376. doi:10.1016/j.gaitpost.2019.12.028

ImportancePhysical activity is associated with cognitive health, even in autosomal dominant forms of dementia. Higher physical activity is associated with slowed cognitive and functional declines over time in adults carrying autosomal dominant variants for frontotemporal lobar degeneration (FTLD), but whether axonal degeneration is a potential neuroprotective target of physical activity in individuals with FTLD is unknown.ObjectiveTo examine the association between physical activity and longitudinal neurofilament light chain (NfL) trajectories in individuals with autosomal dominant forms of FTLD.Design, Setting, and ParticipantsThis cohort study included individuals from the ALLFTD Consortium, which recruited patients from sites in the US and Canada. Symptomatic and asymptomatic adults with pathogenic variants in one of 3 common genes associated with FTLD (GRN, C9orf72, or MAPT) who reported baseline physical activity levels and completed annual blood draws were assessed annually for up to 4 years. Genotype, clinical measures, and blood draws were collected between December 2014 and June 2019; data were analyzed from August 2021 to January 2022. Associations between reported baseline physical activity and longitudinal plasma NfL changes were assessed using generalized linear mixed-effects models adjusting for baseline age, sex, education, functional severity, and motor symptoms.ExposuresBaseline physical activity levels reported via the Physical Activity Scale for the Elderly. To estimate effect sizes, marginal means were calculated at 3 levels of physical activity: 1 SD above the mean represented high physical activity, 0 SD represented average physical activity, and 1 SD below the mean represented low physical activity.Main Outcomes and MeasuresAnnual plasma NfL concentrations were measured with single-molecule array technology.ResultsOf 160 included FTLD variant carriers, 84 (52.5%) were female, and the mean (SD) age was 50.7 (14.7) years. A total of 51 (31.8%) were symptomatic, and 77 carried the C9orf72 variant; 39, GRN variant; and 44, MAPT variant. Higher baseline physical activity was associated with slower NfL trajectories over time. On average, NfL increased 45.8% (95% CI, 22.5 to 73.7) over 4 years in variant carriers. Variant carriers with high physical activity demonstrated 14.0% (95% CI, −22.7 to −4.3) slower NfL increases compared with those with average physical activity and 30% (95% CI, −52.2 to −8.8) slower NfL increases compared with those with low physical activity. Within genotype, C9orf72 and MAPT carriers with high physical activity evidenced 18% to 21% (95% CI, −43.4 to −7.2) attenuation in NfL, while the association between physical activity and NfL trajectory was not statistically significant in GRN carriers. Activities associated with higher cardiorespiratory and cognitive demands (sports, housework, and yardwork) were most strongly correlated with slower NfL trajectories (vs walking and strength training).Conclusions and RelevanceIn this study, higher reported physical activity was associated with slower progression of an axonal degeneration marker in individuals with autosomal dominant FTLD. Physical activity may serve as a primary prevention target in FTLD.

Introduction: Cerebrovascular disease (CVD) and neuropsychiatric symptoms (NPS) are common in older adults and are independently associated with cognitive impairment. In addition, vascular depression (a late-life depression) is hypothesized to be associated with cerebrovascular pathology. Hypothesis: CVD pathology imaging biomarkers (e.g., white matter hyperintensities (WMH), infarctions) are associated with depression and anxiety, and both (depression/anxiety and CVD imaging biomarkers) are independently associated with mild cognitive impairment (MCI). Methods: This cross-sectional study included 1739 Mayo Clinic Study of Aging participants (≥50 years old) without dementia, with comprehensive cognitive evaluations and available data on the Beck Depression Inventory II (BDI), the Beck Anxiety Inventory (BAI) and imaging CVD biomarkers via FLAIR-MRI (i.e., WMH% of total intracranial volume (TIV) and brain infarctions). We used linear and logistic regression models to examine the associations adjusting for age, sex, education, and apolipoprotein E ε4 status. Results: Participants’ mean age (SD) was 71.11 (10.61) years (53.3% males). Higher WMH% TIV burden was significantly associated with higher BDI (b= 0.082, 95%CI: 0.031, 0.133), p=0.002) and BAI scores (b= 0.088, 95%CI: 0.037, 0.140), p=0.001); the presence of infarctions was also associated with a higher BDI score. Both WMH %TIV burden (OR: 1.20 (95%CI:1.05, 1.38), p=0.008) and BDI score (OR: 1.38 (95%CI:1.21, 1.57), p<0.001) were significantly associated with MCI when included simultaneously in the model. We observed similar associations for CVD biomarkers and BAI score with MCI. Conclusions: CVD imaging biomarkers and NPS, as measured by BDI and BAI scores, could represent two distinct processes associated with cognitive impairment. Studies are ongoing to further examine these associations and delineate how CVD, NPS, and other pathophysiology processes (e.g., Alzheimer’s disease) interact and are associated with cognitive impairment.