Walter K. Kremers

Background and ObjectivesPlasma growth differentiation factor 15 (GDF15), a TGFbeta family cytokine, has been robustly identified from proteomics discovery work as linked to adverse brain aging; im...

Sleep and circadian disturbances are common yet understudied in frontotemporal dementia (FTD). Advances in non-invasive digital monitoring technology enable capture of real-time objective nighttime behaviors in naturalistic settings. We examined feasibility and utility of an unobtrusive, under-the-mattress sensor to monitor long-term bed behaviors in a sample of adults with FTD and their study partners. This longitudinal observational study continuously monitored bed behaviors in participants with FTD recruited from the ALLFTD study for up to two years using an Emfit^™ Movement Monitor. Metrics were derived from each session in bed: duration in bed, time of bed-entry/exit, number of tosses/turns, and number of bed-exits. Bivariate relationships between baseline bed behaviors (aggregated across the first 30 days) and clinical characteristics (FTD vs. control; clinical dementia rating) were tested. Linear mixed-effects regressions examined longitudinal associations between baseline clinical severity and bed behaviors over time. To examine differential seasonal shifts in nightly bed behaviors, Fourier basis functions modeled nightly duration in bed over a full calendar year separately by clinical severity groups (controls vs. mild FTD vs. moderate-to-severe FTD). Participants were 16 adults with FTD (mean age = 67; 69% male) and 12 study partner comparators (mean age = 62; 33% male). At baseline, participants with FTD had higher average durations in bed (mean difference=1.8, 95%CI [0.03, 3.61], p=0.046), more variability in durations in bed across days (mean difference=1.8, 95%CI [0.14, 3.37], p=0.035), and more bed exits than study partners (log-transformed; mean difference=0.5, 95%CI [0.02, 1.06], p=0.042). Longitudinally, person-specific linear trajectories of bed behaviors revealed that higher baseline clinical severity associated with significantly steeper increases in tosses/turns over time (β=0.15, 95%CI [0.05, 0.25], p=0.009). Those with more severe FTD also had an attenuated sleep-behavioral response to seasonal daylight shifts (β=0.23, 95%CI [0.13, 0.33], p<0.001). Findings demonstrate the feasibility of long-term, non-invasive nighttime behavior monitoring using bed sensors, highlighting their utility to monitor disease progression in FTD with potential to detect person-specific changes and assess treatment effects. Further research is needed to explore underlying mechanisms of sleep disturbances in FTD and develop targeted interventions to improve sleep and subsequent quality of life for individuals with FTD.

Engagement in physical and cognitive activities is associated with a decreased risk of mild cognitive impairment (MCI) and dementia, but the association with Alzheimer disease (AD) neuroimaging biomarkers is less clear. We thus examined associations of physical and cognitive activities with longitudinal trajectories of AD neuroimaging biomarkers among older adults free of dementia. We conducted a longitudinal study within the population-based Mayo Clinic Study of Aging (mean follow-up durations 1.3-3.4 years). Participants were aged 50 years or older and were cognitively unimpaired or had MCI at baseline. Engagement in physical and cognitive activities during 12 months before baseline was assessed through questionnaires. Participants underwent AD neuroimaging biomarker assessments at 1 or more time points. We ran linear mixed-effect models to examine associations between physical and cognitive activity composite scores and trajectories of individual yearly change in amyloid deposition (Pittsburgh compound B [PiB]-PET centiloid), tau burden (tau-PET standardized uptake value ratio [SUVR]), and regional glucose hypometabolism (fluorodeoxyglucose [FDG]-PET SUVR), adjusted for age, sex, APOE ɛ4 carrier status, and medical comorbidity. We included 1,176 participants (47% female; mean [SD] age, 68.7 [9.6] years) for PiB-PET trajectories, 399 participants (49% female; mean [SD] age, 71.9 [11.0] years) for tau-PET trajectories, and 983 participants (46% female; mean [SD] age, 67.9 [9.2] years) for FDG-PET trajectories. PiB-PET and tau-PET measures increased during follow-up (3.4 [SD 4.0] and 1.3 [SD 2.1] years, respectively), whereas FDG-PET values decreased over 2.9 (SD 3.5) years of follow-up. Participants with higher total physical activity (interaction estimate 0.0017; 95% CI 0.0003-0.0031; p = 0.021) and higher moderate-to-vigorous physical activity (interaction estimate 0.0015; 95% CI 0.0001-0.0029; p = 0.040) had a less pronounced decrease in FDG-PET over time. Participants with higher cognitive activity experienced a less pronounced increase in PiB-PET (interaction estimate -0.2253; 95% CI -0.4437 to -0.0070; p = 0.043) and a smaller decrease in FDG-PET (interaction estimate 0.0015; 95% CI 0.0001-0.0028; p = 0.038) over time. Physical activity was associated with less synaptic dysfunction and cognitive activity with less synaptic dysfunction and lower amyloid burden over time, albeit effect sizes were small. Further research is needed to validate findings and clarify causal inference between physical and cognitive activities and AD neuroimaging biomarkers.

Cellular senescence is a biological mechanism of aging and age‐related diseases. The aim of this study was to examine whether senescence biomarkers are associated with frailty and physical function trajectories in patients undergoing kidney transplantation (KT). We also discussed the relationship between senescence biomarkers and KT function. In this multicenter study, we prospectively assessed plasma levels of senescence biomarkers, frailty as measured by the Physical Frailty Phenotype, and physical function as measured by the Short Physical Performance Battery prior to KT. Frailty, physical function, and KT function were also measured 1 year after KT. Variable associations were assessed using Cox and relaxed least absolute shrinkage and selection operation regression. The cohort consisted of 197 participants (mean age 53 ± 13 years, 61.4% male, and 80.2% White race). Higher pre‐KT levels of macrophage‐derived chemokine (MDC/CCL22) and growth differentiation factor‐15 (GDF‐15) were independently associated with less improvement in frailty and/or physical function during the first year after KT. Higher pre‐KT levels tumor necrosis factor receptor superfamily member 6 (FAS) and MMP‐9 levels were independently associated with lower KT function one year after KT. Pre‐KT cellular senescence may contribute to frailty, physical function, and kidney function trajectories during the first year after KT.

Performance‐based measures of frailty are associated with healthcare utilization after kidney transplantation (KT) but require in‐person assessment. A promising alternative is self‐reported frailty. The goal of this study was to examine the ability of performance‐based and self‐reported frailty measures to predict 30‐day rehospitalizations after KT. We conducted a prospective, observational cohort study involving 272 adults undergoing KT at Mayo Clinic in Minnesota, Florida, or Arizona. We simultaneously measured frailty before KT using the physical frailty phenotype (PFP), the short physical performance battery (SPPB), and self‐report (the Patient‐Reported Outcomes Measurement Information System [PROMIS] 4‐item physical function short form v2.0). Both the PFP and self‐reported frailty were independently associated with more than a 2‐fold greater odds of 30‐day rehospitalizations, while the SPPB was not. To our knowledge, this is the first study to assess the prognostic value of all three of the above frailty measures in patients undergoing KT. The PFP is more prognostic than the SPPB when assessing the risk of 30‐day rehospitalizations; self‐reported frailty can complement the PFP but not replace it. However, the 4‐item survey assessing self‐reported frailty represents a simple way to identify patients undergoing KT surgery who would benefit from interventions to lower the risk of rehospitalizations.

Background: Plasma biomarkers for Alzheimer’s disease (AD) such as phosphorylated tau protein 181 (p‐tau 181), neurofilament light chain (NfL), amyloid‐β 42/40 ratio (Aβ42/40), and glial fibrillary acidic protein (GFAP) have shown high performance in the discrimination of those with clinical mild cognitive impairment (MCI) or dementia from cognitively unimpaired individuals, but few population‐based autopsy studies have evaluated the neuropathological correlates of plasma biomarkers.Method: We utilized a population‐based prospective study of 350 participants with autopsy and antemortem plasma biomarker data. Plasma biomarkers Aβ42/40 ratio, p‐tau181, GFAP, and NfL were measured using Quanterix Simoa assays. We predicted AD neuropathological change (ADNC), Braak staging, and neuritic plaque score using these plasma biomarkers and investigated the added utility of cognitive and demographic information. We utilized a variable selection procedure in cross‐validated (CV) logistic regression models by minimizing the Bayesian Information Criterion to identify the best set of predictors for a given outcome among plasma biomarkers, demographic variables, and neuropsychological tests included in the Mayo Clinic Preclinical Alzheimer Cognitive Composite (Mayo‐PACC).Result: ADNC was best predicted with plasma GFAP, NfL, p‐tau181 biomarkers along with APOE ε4 carrier status and Mayo‐PACC cognitive score (CV AUC = 0.821). Braak staging was best predicted using plasma GFAP, p‐tau181, and cognitive scores (CV AUC = 0.788). Neuritic plaque score was best predicted using plasma Aβ42/40 ratio, p‐tau181, GFAP, and NfL biomarkers (CV AUC = 0.778). Inclusion of plasma biomarkers demonstrated significantly better prediction of Braak staging (p<0.01) and neuritic plaque score (p<0.01), but not ADNC (p = 0.13), when compared to models that included only cognitive and demographic information. Univariate models consistently underperformed in comparison to multivariate models.Conclusion: Plasma biomarkers can provide valuable predictive information about the presence of AD pathology, which can facilitate earlier recognition and diagnosis of AD. Incorporation of demographic and cognitive variables along with plasma variables in multivariable models will improve prediction accuracy of AD pathology, specifically Braak staging and ADNC.

Background: Automated face recognition can potentially re‐identify de‐identified research brain MRI, PET, and CT, and direct differences of brain measurements from original and de‐faced images are statistically significant but very minor, i.e. below scan‐rescan differences. Their effects on analyses correlating brain imaging biomarkers with clinical variables remain unknown.Method: We sampled 3 age‐and‐sex‐matched groups (CU, MCI, and clinical AD) from Mayo Clinic aging studies, each with 61 participants with same‐visit 3D T1‐weighted and 3D T2‐FLAIR MRI, PiB amyloid PET, and Flortaucipir tau PET (total n = 183). We automatically measured hippocampal volume, PIB global SUVR, and tau temporal meta‐ROI SUVR using a) our in‐house pipeline with SPM12, MCALT, and ANTs, and b) FreeSurfer 7.3.2 (PETSurfer), and we measured voxels associated with clinical group separation using SPM12. We also measured WMH from FLAIR using a) our in‐house pipeline, b) Lesion Segmentation Tool (LST), and c) FreeSurfer’s Samseg. For each measurement, we calculated pair‐wise group separation AUC values and Spearman’s rho with age and CDR‐SOB. We then de‐faced all PET and MRI using mri_reface, our top‐performing automated de‐facing software that replaces imaged faces with an average face (“re‐facing”), and we repeated all measurements and analyses with de‐faced images. We then compared the magnitudes of clinical correlations with original images and those of de‐faced images.Result: Analyses of original and de‐faced images had very high agreement. Only 2/55 paired comparisons were significant (Tables 1‐2): FreeSurfer‐measured global PIB SUVR from de‐faced images was more correlated with age (rho 0.003 vs ‐0.227, p = 0.046), and Mayo‐measured WMH volume from original images was more correlated with CDR‐SOB (rho 0.321 vs 0.308, p = 0.020). Another comparison‐pair tested p = 0.041 for an AUC change +0.008, which we consider not practically meaningful. All others had p>0.05. In voxel‐based analyses of CU vs AD participants, all comparisons were qualitatively similar between original and de‐faced variants (Figure 1).Conclusion: De‐facing PET and MRI using mri_reface did not significantly affect 53/55 correlations between imaging and clinical variables; of the two affected, one became stronger with re‐faced images. Stronger correlations with clinical variables may be explained by de‐facing’s standardizing face voxels and artifacts that irrelevantly affect brain segmentations.

Background: Memory 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.Method: Assessments 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.Result: Data 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%).Conclusion: These 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.

Background: We 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.Method: We 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.Result: Results 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%.Conclusion: Among 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.