Vassilaki, Maria

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.

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.

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.

The immune system substantially influences age-related cognitive decline and Alzheimer’s disease (AD) progression, affected by genetic and environmental factors. In a Mayo Clinic Study of Aging cohort, we examined how risk factors like APOE genotype, age, and sex affect inflammatory molecules and AD biomarkers in cerebrospinal fluid (CSF). Among cognitively unimpaired individuals over 65 ( N = 298), we measured 365 CSF inflammatory molecules, finding age, sex, and diabetes status predominantly influencing their levels. We observed age-related correlations with AD biomarkers such as total tau, phosphorylated tau-181, neurofilament light chain (NfL), and YKL40. APOE4 was associated with lower Aβ42 and higher SNAP25 in CSF. We explored baseline variables predicting cognitive decline risk, finding age, CSF Aβ42, NfL, and REG4 to be independently correlated. Subjects with older age, lower Aβ42, higher NfL, and higher REG4 at baseline had increased cognitive impairment risk during follow-up. This suggests that assessing CSF inflammatory molecules and AD biomarkers could predict cognitive impairment risk in the elderly.

Abstract Background Hospitalization can impair physical and functional status of older adults, but it is unclear whether these deficits are transient or chronic. This study determined the association between hospitalization of older adults and changes in long-term longitudinal trajectories of 2 measures of physical and functional status: gait speed (GS) and instrumental activities of daily living measured with Functional Activities Questionnaire (FAQ). Method Linear mixed-effects models assessed the association between hospitalization (nonelective vs elective, and surgical vs medical) and outcomes of GS and FAQ score in participants (older than 60 years) enrolled in the Mayo Clinic Study of Aging who had longitudinal assessments. Results Of 4902 participants, 1879 had ≥1 hospital admission. Median GS at enrollment was 1.1 m/s. The slope of the annual decline in GS before hospitalization was −0.015 m/s. The parameter estimate (95% CI) for additional annual change in GS trajectory after hospitalization was −0.009 (−0.011 to −0.006) m/s, p &lt; .001. The accelerated GS decline was greater for medical versus surgical hospitalizations (−0.010 vs −0.003 m/s, p = .005), and nonelective versus elective hospitalizations (−0.011 vs −0.006 m/s, p = .067). The odds of a worsening FAQ score increased on average by 4% per year. Following hospitalization, odds of FAQ score worsening further increased (multiplicative annual increase in odds ratio per year [95% CI] following hospitalization was 1.05 [1.03, 1.07], p &lt; .001). Conclusions Hospitalization of older adults is associated with accelerated long-term decline in GS and functional limitations, especially after nonelective admissions and those for medical indications. However, for most well-functioning participants, these changes have little clinical significance.

IMPORTANCE Hospitalizations are associated with cognitive decline in older adults. OBJECTIVE To determine the association between hospitalization characteristics and the trajectory of cognitive function in older adults. DESIGN Population‐based longitudinal study of cognitive aging. SETTING Olmsted Medical Center and Mayo Clinic, the only centers in Olmsted County, Minnesota, with hospitalization capacity. PARTICIPANTS Individuals without dementia at baseline, with consecutive cognitive assessments from 2004 through 2017, and at least one visit after the age of 60. MEASUREMENTS The primary outcome was longitudinal changes in global cognitive z ‐score. Secondary outcomes were changes in four cognitive domains: memory, attention/executive function, language, and visuospatial skills. Hospitalization characteristics analyzed included elective versus nonelective, medical versus surgical, critical care versus no critical care admission, and long versus short duration admissions. RESULTS Of 4,587 participants, 1,622 had 1 and more hospital admission. Before hospitalization, the average slope of the global z ‐score was −0.031 units/year. After hospitalization, the rate of annual global z ‐score accelerated by −0.051 (95% CI = −0.057, −0.045) units, P < .001, resulting in an estimated annual slope after the first hospitalization of −0.082. The accelerated decline was found in all four cognitive domains (memory, visuospatial, language, and executive, all P < .001). The acceleration of the decline in global z ‐score following hospitalization was greater for medical compared to surgical hospitalizations (slope change following hospitalization = −0.064 vs −0.034 for medical vs surgical, P < .001), and nonelective compared to elective admissions (slope change following hospitalization = −0.075 vs −0.037 for nonelective vs elective, P < .001). The acceleration of cognitive decline was not different for hospitalization with intensive care unit admission versus not. CONCLUSIONS Hospitalization of older adults is associated with accelerated decline of global and domain‐specific cognitive domains, with the rate of decline dependent upon type of admission. The clinical impact of this accelerated decline will depend on the individual's baseline cognitive reserve and expected longevity.