Vassilaki, Maria

AbstractBackgroundInexpensive, non‐invasive tests may improve the identification of persons at increased risk for cognitive decline and dementia. We compared impairment in odor identification and global cognition with neuro‐imaging biomarkers to predict cognitive decline and dementia in the population‐based Mayo Clinic Study of Aging (MCSA).MethodAt the 2008 assessment, 647 participants who were ≥ 55 years old with at least one follow‐up had the following procedures: modified Blessed Information‐Memory‐Concentration Test (BIMCT), 12‐item Brief Smell Identification Test (BSIT), brain magnetic resonance imaging (MRI), and Positron Emission Tomography (PET) amyloid imaging with 11C‐Pittsburgh compound B (11C‐PiB). A subsample (n=556) also had 18F‐fluorodeoxyglucose (18F‐FDG) PET scans. The utility of these baseline measures to predict cognitive decline (cognitively unimpaired to mild cognitive impairment [MCI] or dementia, or MCI to dementia) was examined during follow‐up (mean 8.1 SD 3.4 years). Transition to dementia was a secondary outcome.ResultAmong 647 participants without dementia (mean age 74.0 SD 8.1 years; 54.4% male) at baseline, 102 participants manifested cognitive decline and 34 participants transitioned to dementia. In Cox regression survival analyses for cognitive decline, PiB PET showed the strongest predictive utility (hazard ratio HR 5.05, 95% CI = 3.32‐7.69, p<.001). Impaired BSIT (HR 3.46, 95% CI = 2.34‐5.11, p<.001), impaired BIMCT (HR 3.63, 95% CI 2.46‐5.36, p<.001), MRI and FDG measures were also significant predictors. After adjusting for age, sex and education, these predictors remained significant with attenuated effects (PiB HR 3.89, 95% CI 2.54‐5.95, p<.001; BSIT HR 2.77, 95% CI 1.85‐4.14, p<.001; BIMCT HR 3.3, 95% CI 2.21‐4.95, p<.001). After adding these demographic variables in the model, the combination of BSIT and BIMCT showed strong predictive utility (C‐index=0.85), similar to PiB PET (C‐index=0.84). Combining all predictors showed a C‐index of 0.89. These measures showed similar but stronger results for dementia transition.ConclusionTests of global cognition and odor identification provide a brief, non‐invasive, inexpensive method comparable to PET amyloid imaging for identifying community‐dwelling persons likely to decline cognitively or transition to dementia. Other potential uses for this combination include identification of persons likely to benefit from disease‐modifying medications and determination of eligibility for prevention trials.

AbstractBackgroundChronic medical conditions increase dementia risk, and the mechanism may be captured computationally by measures of accelerated brain aging. We hypothesized that quantifying the impact of chronic conditions on GM and WM aging will aid in measuring the differential impact of each condition on brain aging and shed light on the mechanisms through which they increase dementia risk. We specifically focused on microstructural brain changes with Neurite Orientation Dispersion and Density Imaging (NODDI) based on advanced diffusion MRI ‐ neurite density index (NDI), free water fraction (FWF), and orientation dispersion index (ODI).MethodWe identified 1123 participants [913 cognitively unimpaired (CU), 114 with MCI, 96 with AD] from the Mayo Clinic Study of Aging (MCSA) and Mayo ADRC with multi‐shell diffusion MRI. MCSA participants had chronic medical conditions’ diagnosis (based on US Dept of HHS) extracted from the electronic health care records. Using the BorutaSHAP algorithm on NDI, ODI, and ISOVF features from GM and WM separately, we constructed two distinct brain age models for GM and WM. First, we evaluated the usefulness of the predicted age gap (PAG), which is the difference between predicted age by model and chronological age, for both WM and GM NODDI models in CU/MCI/AD. We then developed separate GM and WM models for each condition using samples from condition‐negative participants to compare PAG of GM and WM for that condition.ResultThere was greater PAG in GM (avg = ) than WM (avg = 9.15 yrs) in MCI/AD participants (Fig. 1A). Each condition shifted the GM PAG by 0.10 to 3.56 years and WM PAG by 0.38 to 3.72 years with stroke and congestive heart failure showing the greater PAG overall for GM and WM PAD respectively (Fig. 2). COPD, stroke, chronic kidney disease, hypertension had comparatively greater GM PAG than WM PAG; while hyperlipidemia, congestive heart failure and coronary artery disease had greater WM PAG than GM PAG (Fig. 1B).ConclusionChronic conditions are associated with increased GM and WM Brain aging models can aid in quantifying the impact of chronic conditions on dementia risk and understanding mechanisms.

AbstractBackgroundChronic medical conditions increase dementia risk, and the mechanism may be captured computationally by measures of accelerated brain aging. We hypothesized that quantifying the impact of chronic conditions on GM and WM aging will aid in measuring the differential impact of each condition on brain aging and shed light on the mechanisms through which they increase dementia risk. We specifically focused on microstructural brain changes with Neurite Orientation Dispersion and Density Imaging (NODDI) based on advanced diffusion MRI ‐ neurite density index (NDI), free water fraction (FWF), and orientation dispersion index (ODI).MethodWe identified 1123 participants [913 cognitively unimpaired (CU), 114 with MCI, 96 with AD] from the Mayo Clinic Study of Aging (MCSA) and Mayo ADRC with multi‐shell diffusion MRI. MCSA participants had chronic medical conditions’ diagnosis (based on US Dept of HHS) extracted from the electronic health care records. Using the BorutaSHAP algorithm on NDI, ODI, and ISOVF features from GM and WM separately, we constructed two distinct brain age models for GM and WM. First, we evaluated the usefulness of the predicted age gap (PAG), which is the difference between predicted age by model and chronological age, for both WM and GM NODDI models in CU/MCI/AD. We then developed separate GM and WM models for each condition using samples from condition‐negative participants to compare PAG of GM and WM for that condition.ResultThere was greater PAG in GM (avg = ) than WM (avg = 9.15 yrs) in MCI/AD participants (Fig. 1A). Each condition shifted the GM PAG by 0.10 to 3.56 years and WM PAG by 0.38 to 3.72 years with stroke and congestive heart failure showing the greater PAG overall for GM and WM PAD respectively (Fig. 2). COPD, stroke, chronic kidney disease, hypertension had comparatively greater GM PAG than WM PAG; while hyperlipidemia, congestive heart failure and coronary artery disease had greater WM PAG than GM PAG (Fig. 1B).ConclusionChronic conditions are associated with increased GM and WM Brain aging models can aid in quantifying the impact of chronic conditions on dementia risk and understanding mechanisms.

AbstractBackgroundThe immune system is substantially involved in the development and progression of age‐related cognitive decline and Alzheimer’s disease (AD).MethodAs genetic and environmental factors interactively impact these conditions, we investigated how risk factors such as APOE genotype, age, and sex influence immune activation markers and AD biomarkers in cerebrospinal fluid (CSF) in elderly individuals enrolled in the Mayo Clinic Study of Aging cohort. Among cognitively unimpaired individuals aged over 65 at the baseline visit (N=298), we measured 365 CSF immune activation markers using the proximity extension assay.ResultWe found that age, sex, and diabetes status are associated with altered CSF levels of immune activation markers independently of other factors. For CSF AD biomarkers, we observed significant positive correlations between age and total tau, phosphorylated tau‐181 (p‐tau181), neurofilament light (NfL), and YKL40. APOE4 was also associated with lower Aß42 and higher SNAP25 in CSF. We further examined whether baseline visit variables can predict cognitive decline, represented by the conversion from CDR=0 to CDR>0. We found that age, Aß42, NfL, and REG4 were independently correlated with CDR conversion risk. When the cohort was dichotomized by their median values, older participants with lower Aß42, higher NfL, and higher REG4 at baseline developed cognitive impairment during the follow up with a c‐index of 0.762 while age alone had a c‐index of 0.699.ConclusionTogether, our results suggest that assessing CSF immune activation markers and AD biomarkers can improve the prediction of cognitive impairment risk in the elderly.

AbstractBackgroundPrevious studies have linked impaired odor identification and global cognition with increased risk of cognitive decline and transition to dementia. However, the reverse question remains: if individuals have intact performance on these measures, are they at reduced risk for transition? We aimed to examine the accuracy of intact odor identification and global cognition for identifying lack of transition to dementia/cognitive decline using the population‐based Mayo Clinic Study of Aging and compare their accuracy against and in combination with amyloid PET (Positron Emission Tomography).Methodn = 647 participants age≥55 without dementia completed at baseline the Brief Smell Identification Test (BSIT; ‘Intact’ = 9‐12), Blessed Information‐Memory‐Concentration Test (BIMCT; ‘Intact’ = 18‐20,), and amyloid PET (‘Normal/Intact’ SUVR<1.48). We calculated sensitivity, specificity, positive predictive value, and negative predictive value for these measures, with “lack of transition to dementia” as the gold standard/target. “Lack of cognitive decline” (cognitively unimpaired to mild cognitive impairment [MCI]; cognitively unimpaired to dementia; or MCI to dementia) was a secondary target.Result94.7% of participants (613/647) did not transition to dementia and 84.2% (545/647) did not cognitively decline over 11.25‐year‐follow‐up. The combination of intact BSIT+BIMCT had high rates of lack of transition to dementia (98.3%, 355/361) and lack of cognitive decline (93.1%, 336/361). This combination yielded mixed sensitivity (0.579) and specificity (0.824) though had high positive predictive value (0.983) for identifying lack of transition to dementia. Intact amyloid PET alone exhibited comparable rates of lack of transition to dementia (99.0%, 418/423) and lack of cognitive decline (92.4%, 391/423). When evaluated conjointly, the combination of all three intact measures also had mixed sensitivity (0.421) and specificity (0.962) though higher positive predictive value (PPV = 0.996) for lack of transition to dementia. 99.6% (258/259) of participants with intact/normal status on all three did not transition to dementia and 96.5% (250/259) did not cognitively decline.ConclusionThis replicates prior reports that intact olfaction/global cognition have strong utility for identifying individuals unlikely to develop MCI/dementia. Intact/normal amyloid PET further enhances the positive predictive value. Clinically, identifying individuals at low risk of transition can shorten diagnostic workups, reduce need for early follow‐up visits, and screen patients in‐or‐out of clinical trials.

AbstractBackground and AimsPhysical activity (PA) is associated with higher gait speed. We aimed to examine the associations between PA and change in spatial and temporal gait measures as well as fall risk in community‐dwelling individuals free of dementia.MethodsLongitudinal study among 4173 individuals aged ≥50 years (mean age 71 years; 2078 males; median follow‐up 4 years) enrolled in the Mayo Clinic Study of Aging. Self‐reported late‐life PA was used to calculate overall PA and moderate‐vigorous PA (MVPA) scores. Gait was assessed using GAITRite® and Zeno™ systems. Incident falls information was based on diagnostic codes retrieved from medical records. We ran linear mixed effects models to examine associations between z‐scored PA variables and longitudinal gait parameters, adjusted for age, sex, education, body mass index (BMI), medical comorbidities, and including interactions between PA and time since baseline. In secondary analyses, we calculated Cox Proportional hazard models with age as time scale predicting incident falls by PA, adjusting for sex, education, BMI, medical comorbidities, and falls history.ResultsAt baseline, higher PA was associated with higher velocity (overall PA: estimate 2.9935; MVPA: 2.2961; p < 0.001), higher cadence (overall PA: 1.0665; MVPA: 0.9073; p < 0.001), greater stride length (overall PA: 2.0805; MVPA: 1.4726; p < 0.001), shorter double support time (overall PA: −0.0257; MVPA: −0.0205; p < 0.001), and lower stance time variability (overall PA: −0.0204, p < 0.001; MVPA: −0.0152; p = 0.006). Overall PA was longitudinally associated with less decline in cadence, and MVPA with less increase in intraindividual stance time variability. Overall PA (Hazard ratio 0.892, 95% confidence interval 0.828–0.961, p = 0.003) and MVPA (HR 0.901; 95% CI 0.835–0.973, p = 0.008) were associated with a decreased risk of incident falls.ConclusionLate‐life PA was associated with favorable gait outcomes and decreased risk of incident falls. Thus, late‐life PA may help to maintain gait performance and decrease fall risk in old age.

AbstractINTRODUCTIONWe evaluated impaired odor identification and global cognition as simple, cost‐effective alternatives to neuroimaging biomarkers to predict cognitive decline and dementia in the Mayo Clinic Study of Aging.METHODSSix hundred forty‐seven participants (mean 8.1, standard deviation 3.4 years’ follow‐up) had the following baseline procedures: modified Blessed Information Memory Concentration Test (BIMCT), 12‐item Brief Smell Identification Test (BSIT), structural brain magnetic resonance imaging (MRI), and positron emission tomography (PET) imaging with 11C‐Pittsburgh compound B (11C‐PiB) and fluorodeoxyglucose (FDG; subset).RESULTSCognitive decline developed in 102 participants and dementia in 34 participants. In survival analyses, PiB PET showed robust prediction for cognitive decline. Impaired BSIT, impaired BIMCT, MRI, and FDG measures were also significant predictors. The combination of demographics + BSIT + BIMCT showed strong predictive utility (C‐index 0.81), similar to demographics + PiB PET (C‐index 0.80). Similar but stronger results were obtained for prediction of dementia.DISCUSSIONImpairment in both odor identification test and global cognition was comparable to PiB PET for predicting cognitive decline and dementia.Highlights In 647 participants in the population‐based Mayo Clinic Study of Aging, several clinical markers and biomarkers each predicted cognitive decline or dementia during an average 8 years of follow‐up. The combination of the demographic variables of age, sex, and education with a brief odor identification test (BSIT) and a global cognitive test (Blessed Information Memory Concentration Test) showed strong predictive utility (C‐index 0.81) for cognitive decline that was similar to the demographic variables combined with Pittsburgh Compound B amyloid imaging (C‐index 0.80). Combining a brief odor identification test with a brief cognitive test needs consideration as a simple, cost‐effective option in the clinical assessment of individuals at risk of cognitive decline and dementia, as well as a potential tool to identify individuals who may benefit from disease‐modifying treatments and to screen participants for prevention trials.

Abstract There is increasing interest in Alzheimer’s disease related plasma biomarkers due to their accessibility and scalability. We hypothesized that integrating plasma biomarkers with other commonly used and available participant data (MRI, cardiovascular factors, lifestyle, genetics) using machine learning (ML) models can improve individual prediction of cognitive outcomes. Further, our goal was to evaluate the heterogeneity of these predictors across different age strata. This longitudinal study included 1185 participants from the Mayo Clinic Study of Aging who had complete plasma analyte work-up at baseline. We used the Quanterix Simoa immunoassay to measure neurofilament light, Aβ1–42 and Aβ1–40 (used as Aβ42/Aβ40 ratio), glial fibrillary acidic protein, and phosphorylated tau 181 (p-tau181). Participants’ brain health was evaluated through gray and white matter structural MRIs. The study also considered cardiovascular factors (hyperlipidemia, hypertension, stroke, diabetes, chronic kidney disease), lifestyle factors (area deprivation index, body mass index, cognitive and physical activities), and genetic factors (APOE, single nucleotide polymorphisms, and polygenic risk scores). An ML model was developed to predict cognitive outcomes at baseline and decline (slope). Three models were created: a base model with groups of risk factors as predictors, an enhanced model included socio-demographics, and a final enhanced model by incorporating plasma and socio-demographics into the base models. Models were explained for three age strata: younger than 65 years, 65–80 years, and older than 80 years, and further divided based on amyloid positivity status. Regardless of amyloid status the plasma biomarkers showed comparable performance (R² = 0.15) to MRI (R² = 0.18) and cardiovascular measures (R² = 0.10) when predicting cognitive decline. Inclusion of cardiovascular or MRI measures with plasma in the presence of socio-demographic improved cognitive decline prediction (R² = 0.26 and 0.27). For amyloid positive individuals Aβ42/Aβ40, glial fibrillary acidic protein and p-tau181 were the top predictors of cognitive decline while Aβ42/Aβ40 was prominent for amyloid negative participants across all age groups. Socio-demographics explained a large portion of the variance in the amyloid negative individuals while the plasma biomarkers predominantly explained the variance in amyloid positive individuals (21% to 37% from the younger to the older age group). Plasma biomarkers performed similarly to MRI and cardiovascular measures when predicting cognitive outcomes and combining them with either measure resulted in better performance. Top predictors were heterogeneous between cross-sectional and longitudinal cognition models, across age groups, and amyloid status. Multimodal approaches will enhance the usefulness of plasma biomarkers through careful considerations of a study population’s socio-demographics, brain and cardiovascular health.

Background: Studies that assess cognition prospectively and study in detail anxiety history in the participants’ medical records within the context of brain aging and Alzheimer’s disease are limited. Objective: To examine the associations of anxiety and unspecified emotional distress (UED) acquired throughout a person’s life with prospectively collected cognitive outcomes. Methods: Mayo Clinic Study of Aging participants who were cognitively unimpaired at baseline were included. Anxiety and UED data were abstracted from the medical record using the Rochester Epidemiology Project (REP) resources and were run separately as predictors in our models. The data were analyzed using Cox proportional hazards models for the outcomes of incident mild cognitive impairment (MCI) and dementia and using linear mixed effects models for the outcomes of global and domain specific cognitive z-scores and included key covariates. Results: The study sample ( n = 1,808) had a mean (standard deviation) age of 74.5 (7.3) years and 51.4% were male. Anxiety was associated with increased risk of MCI and dementia and was associated with lower baseline cognitive z-scores and accelerated decline over time in the global, memory, and attention domains. UED was associated with faster decline in all domains except visuospatial but did not show evidence of association with incident cognitive outcomes. These results varied by medication use and timing of anxiety. Conclusions: Anxiety and UED both showed inverse associations with cognition. Utilization of anxiety and UED data from across the life course, as available, from the REP system adds robustness to our results.

Abstract Background Relationships of midlife inflammation with late‐life mobility and influences of chronic health conditions, race, and social determinants of health (SDoH) on these relationships are poorly understood. Methods Among 4758 community‐dwelling participants (41% men, 20% Black), high‐sensitivity C‐reactive protein (hsCRP) was measured over 20+ years: in midlife at study visit 2 (V2: 1990–1992, 47–68 years); at V4 (1996–1998, 53–74 years); and with concurrent late‐life 4‐m gait speed at V5 (2011–2013, 67‐88 years, mean 75 years). SDoH measures included race, the national‐rank area deprivation index, education, and income. We examined associations of late‐life gait speed with midlife hsCRP (V2 continuous and clinically high ≥3 mg/L), with 20‐year hsCRP history from midlife (V2–V5 average continuous hsCRP and clinically high ≥3 mg/L) and with inflammation accumulation (visits and years with high hsCRP). Regression models adjusted for demographic, cardiovascular, and SDoH measures; effect modification by the presence of other common chronic conditions (obesity, diabetes, hypertension) and race were examined, with and without accounting for SDoH. Results High midlife hsCRP was associated with slower late‐life gait speed, even among those without chronic conditions in midlife: −4.6 cm/s (95% CI: −6.4, −2.8). Importantly, sustained high hsCRP was associated with a 20‐year slowing of −10.0 cm/s (−14.9, −5.1) among those who never experienced obesity, diabetes, or hypertension over the 20‐year period. Associations were similar between Black participants, −3.8 cm/s (−6.9, −0.7) and White participants −3.3 (−4.5, −2.2) per interquartile range of midlife hsCRP; effect modifications by chronic conditions and race were unsupported throughout. Results were robust to accounting for SDoH or otherwise; however, worse SDoH was associated with higher inflammation and slower gait speed in both Black and White participants. Conclusions Inflammation in midlife may contribute to clinically meaningful late‐life slowing of gait speed, even among otherwise healthy‐appearing adults and regardless of race and socioeconomic disadvantage. Regular monitoring and interventions for inflammation may be warranted from midlife.