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Differences in Daily Physical Activity by Alzheimer’s Risk Markers Among Older Adults
Abstract
Background
Daily physical activity patterns differ by Alzheimer’s disease (AD) status and might signal cognitive risk. It is critical to understand whether patterns are disrupted early in the AD pathological process. Yet, whether established AD risk markers (β-amyloid [Aβ] or apolipoprotein E-ε4 [APOE-ε4]) are associated with differences in objectively measured activity patterns among cognitively unimpaired older adults is unclear.
Methods
Wrist accelerometry, brain Aβ (+/−), and APOE-ε4 genotype were collected in 106 (Aβ) and 472 (APOE-ε4) participants (mean age 76 [standard deviation{SD}: 8.5) or 75 [SD: 9.2] years, 60% or 58% women) in the Baltimore Longitudinal Study of Aging. Adjusted linear and function-on-scalar regression models examined whether Aβ or APOE-ε4 status was cross-sectionally associated with activity patterns (amount, variability, or fragmentation) overall and by time of day, respectively. Differences in activity patterns by combinations of Aβ and APOE-ε4 status were descriptively examined (n = 105).
Results
There were no differences in any activity pattern by Aβ or APOE-ε4 status overall. Aβ+ was associated with lower total amount and lower within-day variability of physical activity overnight and early evening, and APOE-ε4 carriers had higher total amount of activity in the evening and lower within-day variability of activity in the morning. Diurnal curves of activity were blunted among those with Aβ+ regardless of APOE-ε4 status, but only when including older adults with mild cognitive impairment/dementia.
Conclusions
Aβ+ in cognitively unimpaired older adults might manifest as lower amount and variability of daily physical activity, particularly during overnight/evening hours. Future research is needed to examine changes in activity patterns in larger samples and by other AD biomarkers.
... Assessing cognitive status requires taking into account the in uence of subjective and objective factors of the subject, which is di cult to summarise simply and accurately. The Montreal Cognitive Assessment (MoCA) scale is a screening tool that can detect MCI in a timely manner with high sensitivity and speci city [14]. The content of the scale covers 8 cognitive domains with a total of 30 points, with higher scores indicating more intact cognitive functioning.The MoCA scale has been translated into 83 versions in 51 countries and is available in a variety of English language forms to support repeated assessment of patients. ...
Objectives Chronic Obstructive Pulmonary Disease (COPD) remains a serious public health problem globally, and the mortality rate for older COPD patients with cognitive impairment is almost three times that of older patients with cognitive impairment or COPD. The aim of this study was to construct a nomogram prediction model for the risk of comorbid cognitive impairment in COPD patients and to evaluate its clinical application. It helps to detect cognitive impairment in COPD patients at an early stage and give them effective interventions in time, so as to delay the progression of COPD patients and improve their prognosis.
Methods In this study, COPD patients hospitalised at the North China University of Science and Technology Affiliated Hospitalwere evaluated by the Montreal cognitive assessment (MoCA) scale for cognitive function, and divided into a case group and a control group on the basis of whether or not they were combined with cognitive impairment. Based on the basic characteristics of the patients and the laboratory indexes in the first 24 hours of hospitalisation, we conducted statistical analyses, screened out the risk factors and established the Nomogram Prediction Model by using the R software, and finally, we evaluated the clinical value of the model through the calculation of ROC curves for sensitivity, specificity and kappa value. Finally, the sensitivity, specificity and Kappa value were calculated by ROC curve to evaluate the clinical value of the model.
Results After statistical analysis, C-reactive protein (CRP) and homocysteine (Hcy) were found to be the risk factors for combined cognitive impairment in COPD patients, and the Nomogram prediction model was constructed by combining CRP and Hcy and plotted the ROC curve, and it was found that its model finally screened the critical value of the total score of 62.55, and the area under the ROC curve of the model was 0.870, and the sensitivity was 84.7%, and the specificity was 80.4%, indicating that it has a high degree of consistency with the actual results. The area under the ROC curve of this model was 0.870, the sensitivity was 84.7%, the specificity was 80.4%, and the calculated Kappa value was 0.575, which indicated that the consistency between the prediction results and the actual results was better, and it had a higher clinical application value.
Conclusions CRP and Hcy are closely associated with comorbid cognitive impairment in COPD patients, and increased levels of CRP and Hcy are associated with an increased risk of comorbid cognitive impairment in COPD patients. Combining both CRP and Hcy to create a nomogram model for predicting comorbid cognitive impairment in patients with COPD has good predictive ability.
Study Objectives
To compare sleep and 24-hour rest/activity rhythms (RARs) between cognitively normal older adults who are β-amyloid-positive (Aβ or Aβ- and replicate a novel time-of-day-specific difference between these groups identified in a previous exploratory study.
Methods
We studied 82 cognitively normal participants from the Baltimore Longitudinal Study of Aging (aged 75.7 ±8.5 years, 55% female, 76% White) with wrist actigraphy data and Aβ vs. Aβ- status measured by [11C] Pittsburgh compound B positron emission tomography. RARs were calculated using epoch-level activity count data from actigraphy. We used novel, data-driven function-on-scalar regression (FOSR) analyses and standard RAR metrics to cross sectionally compare RARs between 25 Aβ+ and 57 Aβ- participants.
Results
Compared to Aβ- participants, Aβ+ participants had higher mean activity from 1:00 PM-3:30 PM when using less conservative pointwise confidence intervals (CIs) and from 1:30 PM-2:30 PM using more conservative, simultaneous CIs. Further, Aβ+ participants had higher day-to-day variability in activity from 9:00 AM-11:30 AM and lower variability from 1:30 PM-4:00 PM and 7:30 PM-10:30 PM according to pointwise CIs, and lower variability from 8:30 PM-10:00 PM using simultaneous CIs. There were no Aβ-related differences in standard sleep or RAR metrics.
Conclusions
Findings suggest Aβ older adults have higher, more stable day-to-day afternoon/evening activity than Aβ- older adults, potentially reflecting circadian dysfunction. Studies are needed to replicate our findings and determine whether these or other time-of-day-specific RAR features have utility as markers of preclinical Aβdeposition and if they predict clinical dementia and agitation in the afternoon/evening (i.e., “sundowning”).
Background
Gradual disengagement from daily physical activity (PA) could signal present or emerging mild cognitive impairment (MCI) or Alzheimer’s disease (AD).
Objective
This study examined whether accelerometry-derived patterns of everyday movement differ by cognitive diagnosis in participants of the Baltimore Longitudinal Study of Aging (BLSA).
Methods
Activity patterns, overall and by time-of-day, were cross-sectionally compared between participants with adjudicated normal cognition (n = 549) and MCI/AD diagnoses (n = 36; 5 participants [14%] living with AD) using covariate-adjusted regression models.
Results
Compared to those with normal cognition, those with MCI/AD had 2.1% higher activity fragmentation (SE = 1.0%, p = 0.036) but similar mean total activity counts/day (p = 0.075) and minutes/day spent active (p = 0.174). Time-of-day analyses show MCI/AD participants had lower activity counts and minutes spent active during waking hours (6:00 am–5:59 pm; p < 0.01 for all). Also, they had lower activity fragmentation from 12:00–5:59 am (p < 0.001), but higher fragmentation from 12:00–5:59 pm (p = 0.026).
Conclusion
Differences in the timing and patterns of physical activity throughout the day linked to MCI/AD diagnoses warrant further investigation into potential clinical utility.
Study Objectives
To examine in a subsample at the screening phase of a clinical trial of an β-amyloid (Aβ) antibody whether disturbed sleep and altered 24-hour rest/activity rhythms (RARs) may serve as markers of preclinical Alzheimer’s disease (AD).
Methods
Overall, 26 Aβ-positive (Aβ+) and 33 Aβ-negative (Aβ-) cognitively unimpaired participants (mean age = 71.3±4.6 years, 59% women) from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s (A4) and the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies, respectively, wore actigraphs for 5.66±0.88 24-hour periods. We computed standard sleep parameters, standard RAR metrics (mesor, amplitude, acrophase, interdaily stability, intradaily variability, relative amplitude), and performed a novel RAR analysis (function-on-scalar regression (FOSR)).
Results
We were unable to detect any differences between Aβ+ or Aβ- participants in standard sleep parameters or RAR metrics with our sample size. When we used novel FOSR methods, however, Aβ+ participants had lower activity levels than Aβ- participants in the late night through early morning (11:30 PM to 3:00 AM), and higher levels in the early morning (4:30 AM to 8:30 AM) and from midday through late afternoon (12:30 PM to 5:30 PM; all p <0.05). Aβ+ participants also had higher variability in activity across days from 9:30 PM to 1:00 AM and 4:30 AM to 8:30 AM, and lower variability from 2:30 AM to 3:30 AM (all p <0.05).
Conclusions
Although we found no association of preclinical AD with standard actigraphic sleep or RAR metrics, a novel data-driven analytic method identified temporally “local” RAR alterations in preclinical AD.
Introduction:
Higher energetic costs for mobility predict gait speed decline. Slow gait is linked to cognitive decline and Alzheimer's disease (AD). Whether the energetic cost of walking is linked to AD pathology is unknown. We investigated the cross-sectional association between the energetic cost of walking, gait speed, and amyloid beta (Aβ) status (+/-) in older adults.
Methods:
One hundred forty-nine cognitively normal adults (56% women, mean age 77.5 ± 8.4 years) completed customary-paced walking assessments with indirect calorimetry and 11C-Pittsburgh compound B positron emission tomography. Logistic regression models examined associations adjusted for demographics, body composition, comorbid conditions, and apolipoprotein E ε4.
Results:
Each 0.01 mL/kg/m greater energy cost was associated with 18% higher odds of being Aβ+ (odds ratio [OR] = 1.18; 95% confidence interval [CI]: 1.04 to 1.34; P = .011). These findings were not observed when investigating gait speed (OR = 0.99; 95% CI: 0.97 to 1.01; P = .321).
Discussion:
High energetic cost of walking is linked to AD pathology and may be a potential target for therapeutic intervention.
Introduction:
Few studies have explored whether gait measured continuously within a community setting can identify individuals with Alzheimer's disease (AD). This study tests the feasibility of this method to identify individuals at the earliest stage of AD.
Methods:
Mild AD (n = 38) and cognitively normal control (CNC; n = 48) participants from the University of Kansas Alzheimer's Disease Center Registry wore a GT3x+ accelerometer continuously for 7 days to assess gait. Penalized logistic regression with repeated five-fold cross-validation followed by adjusted logistic regression was used to identify gait metrics with the highest predictive performance in discriminating mild AD from CNC.
Results:
Variability in step velocity and cadence had the highest predictive utility in identifying individuals with mild AD. Metrics were also associated with cognitive domains impacted in early AD.
Discussion:
Continuous gait monitoring may be a scalable method to identify individuals at-risk for developing dementia within large, population-based studies.
Imaging markers of cerebrovascular disease and Alzheimer’s disease (AD) are implicated in mobility impairment in older adults, but few studies have examined these relationships longitudinally in a racially-diverse population-based sample. At Visit 5 (2011–13) of the ARIC Study, 1859 participants had usual pace gait speed (cm/s) assessed and brain MRI (mean age = 76.3, 28.5% Black) and PET (n = 343; mean age = 75.9, 42.6% Black) measures including total/regional brain volume (cm3), white matter hyperintensities (WMH; cm3), infarcts (present/absent), microbleeds (count) and global beta-amyloid (Aβ). Participants returned at Visit 6 (n = 1264, 2016–17) and Visit 7 (n = 1108, 2018–19) for follow-up gait speed assessments. We used linear regression to estimate effects of baseline infarct presence, higher microbleed count, and a one interquartile range (IQR) poorer measures of continuous predictors (−1 IQR total brain volume, temporal-parietal lobe meta region of interest(ROI); +1 IQR WMH volume, global Aβ SUVR) on cross-sectional gait speed and change in gait speed adjusting for age, sex, education, study site, APOE e4, estimated intracranial volume, BMI, and cardiovascular risk factors. Cross-sectionally, slower gait speed outcome was associated with higher WMH volume, −3.38 cm/s (95%CI:-4.71, −2.04), infarct presence, −5.60 cm/s (−7.69, −3.51), microbleed count, −2.20 cm/s (−3.20, −0.91), smaller total brain volume, −9.26 cm/s (−12.1, −6.43), and smaller temporal-parietal lobe ROI -6.28 cm/s (−8.28, −4.28). Longitudinally, faster gait speed outcome decline was associated with higher WMH volume, −0.27 cm/s/year, (−0.51, −0.03) and higher global Aβ SUVR, −0.62 cm/s/year (−1.20, −0.03). Both cerebrovascular and AD pathology may contribute to mobility decline commonly seen with aging.
Background:
Cognition is closely associated with physical function. Although high brain amyloid-β (Aβ) deposition and neurofilament light chain (NfL) are associated with cognitive and gait speed decline, relationships of combined plasma Aβ and NfL profiles with cognitive and physical functions in older adults remain unknown. The research aim of this study was to investigate the prospective associations of combined plasma Aβ and NfL profiles with cognitive and physical functions in older adults.
Methods:
Participants (n = 452, aged 76 ± 5 years) who had both plasma Aβ and NfL data collected from the Multidomain Alzheimer's Preventive Trial (MAPT, May 2008 to April 2016) were included in the current study. These participants were from four MAPT groups (multidomain interventions [physical activity and nutritional counselling, and cognitive training], omega-3 supplementation, multidomain plus omega-3 supplementation and control group) and had received a 3-year intervention, followed by a 2-year observational follow-up. Cognitive function was evaluated as Mini-Mental State Examination and composite cognitive score (CCS, a mean Z-score combining four cognitive tests). Physical function was evaluated as gait speed (4-m usual-pace walk test) and chair-stand time (5-time maximal chair-stand test). Cognitive and physical function data measured at the time of and after blood Aβ and NfL tests were used for analysis. Participants with plasma Aβ42/Aβ40 ratios lower than 0.107 and NfL levels greater than 93.04 pg/ml were classified as Aβ+ and NfL+. Multivariable regressions and mixed-effects linear models were used for the analysis.
Results:
At the cross-sectional level, no significant association was found between Aβ+NfL+ and cognitive or physical function after controlling for age, sex, body mass index, education level and MAPT group. Evaluating longitudinal changes, participants with Aβ+NfL+ had greater annual declines in the CCS (β = - 0.11, 95%CI [- 0.17, - 0.05]) and gait speed (β = - 0.03, 95%CI [- 0.05, - 0.005]). After adjusting for APOE ɛ4 genotype, Aβ+NfL+ was associated with a greater decline only in the CCS (β = - 0.09, 95%CI [- 0.15, - 0.02]).
Conclusions:
Combined low plasma Aβ42/Aβ40 ratio and high plasma NfL level was associated with greater declines in cognition and gait speed over time, providing further evidence of the links between cognitive and physical function.
Trial registration:
www.clinicaltrials.gov [ NCT00672685 ].
Physical activity improves overall health and reduces the risk of many negative health outcomes and may be effective in improving cognition, independent functioning, and psychological health in older adults. Given the evidence linking physical activity with improvements in various aspects of health and functioning, interventions exploring pathways for decreasing risk of dementia in those with mild cognitive impairment (MCI) and improving outcomes for those with dementia are of critical importance. The present review highlights the work examining physical activity interventions in order to achieve a comprehensive understanding of the potential benefits of physical activity for individuals experiencing cognitive decline. The primary focus is on aerobic exercise as this is the main intervention in the literature. Our review supports the thesis that physical activity can promote healthy aging in terms of cognition, independent functioning, and psychological health for individuals experiencing cognitive decline. Specifically, physical activity improves cognition, especially executive functioning and memory in MCI, independent functioning in MCI and dementia, and psychological health in dementia. Given that benefits of physical activity have been observed across these domains, such interventions provide an avenue for preventing decline and/or mitigating impairment across several domains of functioning in older adults with MCI or dementia and may be recommended (and adjusted) for patients across a range of settings, including medical and mental health settings. Further implications for clinical intervention and future directions for research are discussed.
Importance
Fragmented daily physical activity may be a sign of physiological decline that provides more powerful insight into impending mortality than total daily activity.
Objective
To compare and contrast the association between total daily activity and activity fragmentation, which encompasses activity bouts and duration, and mortality risk.
Design, Setting, and Participants
In this cohort study, accelerometer data from 2007 through 2015 and mortality data from 2007 through 2017 were collected from 548 adults aged 65 years and older participating in the Baltimore Longitudinal Study of Aging. The dates of analysis were November 2016 to June 2019, with data collected through December 31, 2017. Using Cox proportional hazards regression, the association between accelerometer-derived patterns of physical activity and mortality was estimated after adjusting for demographic characteristics, lifestyle factors, and comorbidities.
Exposures
Minute-by-minute physical activity data were collected over a 24-hour, 7-day period (excluding times between 11:00 pm and 4:59 am) using an accelerometer. Each minute was labeled either active or sedentary, and 5 features of accelerometer data were extracted: total daily activity (defined as any activity performed throughout the day), activity fragmentation (defined as an active-to-sedentary transition probability), and 3 measures of activity bouts (<5, 5-10, and ≥10 active minutes).
Main Outcomes and Measures
All-cause mortality.
Results
Among 548 well-functioning older adults (mean [SD] age, 75.8 [7.2] years; 262 [47.8%] women), 61 participants (11.1%) died. Total daily physical activity was not associated with mortality risk (hazard ratio [HR], 0.90 [95% CI, 0.75-1.08]; P = .28). However, more fragmented physical activity patterns were associated with greater mortality risk (HR, 1.49 [95% CI, 1.02-2.19]; P = .04) after adjusting for age, sex, race/ethnicity, body mass index, smoking history, employment, self-reported health, grip strength, usual gait speed, comorbidities, and device wear time. In addition, more frequently engaging in activity bouts lasting less than 5 minutes was associated with greater mortality risk (HR, 1.28 [95% CI, 1.01-1.61]; P = .04), whereas activity bouts of 5 to 10 minutes (HR, 0.99 [95% CI, 0.58-1.69]; P = .97) and 10 minutes or longer (HR, 0.81 [95% CI, 0.65-1.01]; P = .06) were not associated with mortality risk.
Conclusions and Relevance
In this cohort study of well-functioning adults aged 65 years and older, fragmented daily physical activity, particularly activity bouts lasting less than 5 minutes, was associated with greater mortality risk. These findings suggest that activity fragmentation in older adults may precede declines in functional capability and overall physical activity that typically indicate impending mortality.
Background
Numerous studies have reported weak or moderate correlations between self-reported and accelerometer-assessed physical activity. One explanation is that self-reported physical activity might be biased by demographic, cognitive or other factors. Cognitive function is one factor that could be associated with either overreporting or underreporting of daily physical activity. Difficulties in remembering past physical activities might result in recall bias. Thus, the current study examines whether the cognitive function is associated with differences between self-reported and accelerometer-assessed physical activity. Methods
Cross-sectional data from the population-based Activity and Function in the Elderly in Ulm study (ActiFE) were used. A total of 1172 community-dwelling older adults (aged 65–90 years) wore a uniaxial accelerometer (activPAL unit) for a week. Additionally, self-reported physical activity was assessed using the LASA Physical Activity Questionnaire (LAPAQ). Cognitive function was measured with four items (immediate memory, delayed memory, recognition memory, and semantic fluency) from the Consortium to Establish a Registry for Alzheimer’s Disease Total Score (CERAD-TS). ResultsMean differences of self-reported and accelerometer-assessed physical activity (MPA) were associated with cognitive function in men (rs = −.12, p = .002) but not in women. Sex-stratified multiple linear regression analyses showed that MPA declined with high cognitive function in men (β = −.13; p = .015). Conclusion
Results suggest that self-reported physical activity should be interpreted with caution in older populations, as cognitive function was one factor that explained the differences between objective and subjective physical activity measurements.
The number of the elderly across the globe will approximate 2.1 billion by 2050. Juxtaposed against this burgeoning segment of the population is evidence that nonpathological aging is associated with an increased risk for cognitive decline in a variety of domains, changes that can cause mild disability even before the onset of dementia. Given that pharmacological treatments that mitigate dementia are still outstanding, alternative therapeutic options are being investigated increasingly. The results from translational studies have shown that modifiable lifestyle factors—including physical activity, cognitive engagement, and diet—are a key strategy for maintaining brain health during aging. Indeed, a multiplicity of studies has demonstrated relationships between lifestyle factors, brain structure and function, and cognitive function in aging adults. For example, physical activity and diet modulate common neuroplasticity substrates (neurotrophic signaling, neurogenesis, inflammation, stress response, and antioxidant defense) in the brain whereas cognitive engagement enhances brain and cognitive reserve. The aims of this review are to evaluate the relationship between modifiable lifestyle factors, neuroplasticity, and optimal brain health during aging; to identify putative mechanisms that contribute positive brain aging; and to highlight future directions for scientists and clinicians. Undoubtedly, the translation of cutting-edge knowledge derived from the field of cognitive neuroscience will advance our understanding and enhance clinical treatment interventions as we endeavor to promote brain health during aging.
Background: Alzheimer’s disease (AD) is a neurodegenerative disease that results in severe disability. Very few studies have explored changes in daily physical activity patterns during early stages of AD when components of physical function and mobility may be preserved.
Objective: Our study explored differences in daily physical activity profiles, independent of the effects of non-cognitive factors including physical function and age, among individuals with mild AD compared to controls.
Methods: Patients with mild AD and controls (n = 92) recruited from the University of Kansas Alzheimer’s Disease Center Registry, wore the Actigraph GT3X+ for seven days, and provided objective physical function (VO2 max) and mobility data. Using multivariate linear regression, we explored whether individuals with mild AD had different daily average and diurnal physical activity patterns compared to controls independent of non-cognitive factors that may affect physical activity, including physical function and mobility.
Results: We found that mild AD was associated with less moderate-intensity physical activity (p < 0.05), lower peak activity (p < 0.01), and lower physical activity complexity (p < 0.05) particularly during the morning. Mild AD was not associated with greater sedentary activity or less lower-intensity physical activity across the day after adjusting for non-cognitive covariates.
Conclusions: These findings suggest that factors independent of physical capacity and mobility may drive declines in moderate-intensity physical activity, and not lower-intensity or sedentary activity, during the early stage of AD. This underscores the importance of a better mechanistic understanding of how cognitive decline and AD pathology impact physical activity. Findings emphasize the potential value of designing and testing time-of-day specific physical activity interventions targeting individuals in the early stages of AD, prior to significant declines in mobility and physical function.
Physically active lifestyles and other health-enhancing behaviors play an important role in preserving executive function into old age. Conversely, emerging research suggests that executive functions facilitate participation in a broad range of healthy behaviors including physical activity and reduced fatty food, tobacco, and alcohol consumption. They do this by supporting the volition, planning, performance monitoring, and inhibition necessary to enact intentions and override urges to engage in health damaging behavior. Here, we focus firstly on evidence suggesting that health-enhancing behaviors can induce improvements in executive function. We then switch our focus to findings linking executive function to the consistent performance of health-promoting behaviors and the avoidance of health risk behaviors. We suggest that executive function, health behavior, and disease processes are interdependent. In particular, we argue that a positive feedback loop may exist whereby health behavior-induced changes in executive function foster subsequent health-enhancing behaviors, which in turn help sustain efficient executive functions and good health. We conclude by outlining the implications of this reciprocal relationship for intervention strategies, the design of research studies, and the study of healthy aging.
It is important to characterize the temporal trajectories of disease-related biomarkers in order to monitor progression and identify potential points of intervention. This is especially important for neurodegenerative diseases, as therapeutic intervention is most likely to be effective in the preclinical disease stages prior to significant neuronal damage. Longitudinal neuroimaging allows for the measurement of structural, functional, and metabolic integrity of the brain over time at the level of voxels. However, commonly used longitudinal analysis approaches, such as linear mixed effects models, do not account for the fact that individuals enter a study at various disease stages and progress at different rates, and generally consider each voxelwise measure independently. We propose a multivariate nonlinear mixed effects model for estimating the trajectories of voxelwise neuroimaging biomarkers from longitudinal data that accounts for such differences across individuals. The method involves the prediction of a progression score for each visit based on a collective analysis of voxelwise biomarker data within an expectation-maximization framework that efficiently handles large amounts of measurements and variable number of visits per individual, and accounts for spatial correlations among voxels. This score allows individuals with similar progressions to be aligned and analyzed together, which enables the construction of a trajectory of brain changes as a function of an underlying progression or disease stage. Application of our method to studying images of beta-amyloid deposition, a hallmark of preclinical Alzheimer's disease, suggests that precuneus is the earliest cortical region to accumulate amyloid. The proposed method can be applied to other types of longitudinal imaging data, including metabolism, blood flow, tau, and structural imaging-derived measures.
At the 67th Gerontological Society of America Annual Meeting, a preconference workshop was convened to discuss the challenges of accurately assessing physical activity in older populations. The advent of wearable technology (eg, accelerometers) to monitor physical activity has created unprecedented opportunities to observe, quantify, and define physical activity in the real-world setting. These devices enable researchers to better understand the associations of physical activity with aging, and subsequent health outcomes. However, a consensus on proper methodological use of these devices in older populations has not been established. To date, much of the validation research regarding device type, placement, and data interpretation has been performed in younger, healthier populations, and translation of these methods to older populations remains problematic. A better understanding of these devices, their measurement properties, and the data generated is imperative to furthering our understanding of daily physical activity, its effects on the aging process, and vice versa. The purpose of this article is to provide an overview of the highlights of the preconference workshop, including properties of the different types of accelerometers, the methodological challenges of employing accelerometers in older study populations, a brief summary of ongoing aging-related research projects that utilize different types of accelerometers, and recommendations for future research directions.
Cerebral amyloid-β aggregation is an early pathological event in Alzheimer disease (AD), starting decades before dementia onset. Estimates of the prevalence of amyloid pathology in persons without dementia are needed to understand the development of AD and to design prevention studies.
To use individual participant data meta-analysis to estimate the prevalence of amyloid pathology as measured with biomarkers in participants with normal cognition, subjective cognitive impairment (SCI), or mild cognitive impairment (MCI).
Relevant biomarker studies identified by searching studies published before April 2015 using the MEDLINE and Web of Science databases and through personal communication with investigators.
Studies were included if they provided individual participant data for participants without dementia and used an a priori defined cutoff for amyloid positivity.
Individual records were provided for 2914 participants with normal cognition, 697 with SCI, and 3972 with MCI aged 18 to 100 years from 55 studies.
Prevalence of amyloid pathology on positron emission tomography or in cerebrospinal fluid according to AD risk factors (age, apolipoprotein E [APOE] genotype, sex, and education) estimated by generalized estimating equations.
The prevalence of amyloid pathology increased from age 50 to 90 years from 10% (95% CI, 8%-13%) to 44% (95% CI, 37%-51%) among participants with normal cognition; from 12% (95% CI, 8%-18%) to 43% (95% CI, 32%-55%) among patients with SCI; and from 27% (95% CI, 23%-32%) to 71% (95% CI, 66%-76%) among patients with MCI. APOE-ε4 carriers had 2 to 3 times higher prevalence estimates than noncarriers. The age at which 15% of the participants with normal cognition were amyloid positive was approximately 40 years for APOE ε4ε4 carriers, 50 years for ε2ε4 carriers, 55 years for ε3ε4 carriers, 65 years for ε3ε3 carriers, and 95 years for ε2ε3 carriers. Amyloid positivity was more common in highly educated participants but not associated with sex or biomarker modality.
Among persons without dementia, the prevalence of cerebral amyloid pathology as determined by positron emission tomography or cerebrospinal fluid findings was associated with age, APOE genotype, and presence of cognitive impairment. These findings suggest a 20- to 30-year interval between first development of amyloid positivity and onset of dementia.
Physically active lifestyles contribute to better executive function. However, it is unclear whether high levels of executive function lead people to be more active. This study uses a large sample and multi-wave data to identify whether a reciprocal association exists between physical activity and executive function. Participants were 4555 older adults tracked across four waves of the English Longitudinal Study of Aging. In each wave executive function was assessed using a verbal fluency test and a letter cancelation task and participants reported their physical activity levels. Fixed effects regressions showed that changes in executive function corresponded with changes in physical activity. In longitudinal multilevel models low levels of physical activity led to subsequent declines in executive function. Importantly, poor executive function predicted reductions in physical activity over time. This association was found to be over 50% larger in magnitude than the contribution of physical activity to changes in executive function. This is the first study to identify evidence for a robust bidirectional link between executive function and physical activity in a large sample of older adults tracked over time.
Functional data analysis (FDA) involves the analysis of data whose ideal
units of observation are functions defined on some continuous domain, and the
observed data consist of a sample of functions taken from some population,
sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the
development of this field, which has accelerated in the past 10 years to become
one of the fastest growing areas of statistics, fueled by the growing number of
applications yielding this type of data. One unique characteristic of FDA is
the need to combine information both across and within functions, which Ramsay
and Silverman called replication and regularization, respectively. This article
will focus on functional regression, the area of FDA that has received the most
attention in applications and methodological development. First will be an
introduction to basis functions, key building blocks for regularization in
functional regression methods, followed by an overview of functional regression
methods, split into three types: [1] functional predictor regression
(scalar-on-function), [2] functional response regression (function-on-scalar)
and [3] function-on-function regression. For each, the role of replication and
regularization will be discussed and the methodological development described
in a roughly chronological manner, at times deviating from the historical
timeline to group together similar methods. The primary focus is on modeling
and methodology, highlighting the modeling structures that have been developed
and the various regularization approaches employed. At the end is a brief
discussion describing potential areas of future development in this field.
In spite of evidence that physical activity has beneficial effects on health and age-related functional decline, there is a scarcity of detailed and accurate information on objectively measured daily activity and patterns of such activity in older adults.
Participants in the Baltimore Longitudinal Study of Aging (n = 611, 50% male, mean age 67, range 32-93) wore the Actiheart portable activity monitor for 7 days in the free-living environment. The association between activity and age was modeled using a continuous log-linear regression of activity counts on age with sex, body mass index, employment status, functional performance, and comorbid conditions as covariates.
In the fully adjusted model, continuous analyses demonstrated that overall physical activity counts were 1.3% lower for each year increase in age. Although there were no differences among morning levels of activity, there was significantly lower afternoon and evening activity in older individuals (p < .01). After adjusting for age, poor functional performance, nonworking status, and higher body mass index were independently associated with less physical activity (p < .001).
The use of accelerometers to characterize minute-by-minute intensity, cumulative physical activity counts, and daily activity patterns provides detailed data not gathered by traditional subjective methods, particularly at low levels of activity. The findings of a 1.3% decrease per year in activity from mid-to-late life, and the corresponding drop in afternoon and evening activity, provide new information that may be useful when targeting future interventions. Further, this methodology addresses essential gaps in understanding activity patterns and trends in more sedentary sectors of the population.
Importance:
Older adults commonly report disturbed sleep, and recent studies in humans and animals suggest links between sleep and Alzheimer disease biomarkers. Studies are needed that evaluate whether sleep variables are associated with neuroimaging evidence of β-amyloid (Aβ) deposition. OBJECTIVE To determine the association between self-reported sleep variables and Aβ deposition in community-dwelling older adults.
Design, setting, and participants:
Cross-sectional study of 70 adults (mean age, 76 [range, 53-91] years) from the neuroimaging substudy of the Baltimore Longitudinal Study of Aging, a normative aging study. EXPOSURE Self-reported sleep variables. MAIN OUTCOMES AND MEASURES β-Amyloid burden, measured by carbon 11-labeled Pittsburgh compound B positron emission tomography distribution volume ratios (DVRs).
Results:
After adjustment for potential confounders, reports of shorter sleep duration were associated with greater Aβ burden, measured by mean cortical DVR (B = 0.08 [95% CI, 0.03-0.14]; P = .005) and precuneus DVR (B = 0.11 [0.03-0.18]; P = .007). Reports of lower sleep quality were associated with greater Aβ burden measured by precuneus DVR (B = 0.08 [0.01-0.15]; P = .03).
Conclusions and relevance:
Among community-dwelling older adults, reports of shorter sleep duration and poorer sleep quality are associated with greater Aβ burden. Additional studies with objective sleep measures are needed to determine whether sleep disturbance causes or accelerates Alzheimer disease.
Mobility limitations are common and hazardous in community-dwelling older adults but are largely understudied, particularly regarding the role of the central nervous system (CNS). This has limited development of clearly defined pathophysiology, clinical terminology, and effective treatments. Understanding how changes in the CNS contribute to mobility limitations has the potential to inform future intervention studies.
A conference series was launched at the 2012 conference of the Gerontological Society of America in collaboration with the National Institute on Aging and the University of Pittsburgh. The overarching goal of the conference series is to facilitate the translation of research results into interventions that improve mobility for older adults.
Evidence from basic, clinical, and epidemiological studies supports the CNS as an important contributor to mobility limitations in older adults without overt neurologic disease. Three main goals for future work that emerged were as follows: (a) develop models of mobility limitations in older adults that differentiate aging from disease-related processes and that fully integrate CNS with musculoskeletal contributors; (b) quantify the contribution of the CNS to mobility loss in older adults in the absence of overt neurologic diseases; (c) promote cross-disciplinary collaboration to generate new ideas and address current methodological issues and barriers, including real-world mobility measures and life-course approaches.
In addition to greater cross-disciplinary research, there is a need for new approaches to training clinicians and investigators, which integrate concepts and methodologies from individual disciplines, focus on emerging methodologies, and prepare investigators to assess complex, multisystem associations.
Apolipoprotein E (Apo-E) is a major cholesterol carrier that supports lipid transport and injury repair in the brain. APOE polymorphic alleles are the main genetic determinants of Alzheimer disease (AD) risk: individuals carrying the ε4 allele are at increased risk of AD compared with those carrying the more common ε3 allele, whereas the ε2 allele decreases risk. Presence of the APOE ε4 allele is also associated with increased risk of cerebral amyloid angiopathy and age-related cognitive decline during normal ageing. Apo-E-lipoproteins bind to several cell-surface receptors to deliver lipids, and also to hydrophobic amyloid-β (Aβ) peptide, which is thought to initiate toxic events that lead to synaptic dysfunction and neurodegeneration in AD. Apo-E isoforms differentially regulate Aβ aggregation and clearance in the brain, and have distinct functions in regulating brain lipid transport, glucose metabolism, neuronal signalling, neuroinflammation, and mitochondrial function. In this Review, we describe current knowledge on Apo-E in the CNS, with a particular emphasis on the clinical and pathological features associated with carriers of different Apo-E isoforms. We also discuss Aβ-dependent and Aβ-independent mechanisms that link Apo-E4 status with AD risk, and consider how to design effective strategies for AD therapy by targeting Apo-E.
Previous studies suggest physical activity improves cognition and lowers Alzheimer's disease (AD) risk. However, key AD pathogenic factors that are thought to be influenced by physical activity, particularly plasma amyloid-β (Aβ) and Aβ brain load, have yet to be thoroughly investigated. The objective of this study was to determine if plasma Aβ and amyloid brain deposition are associated with physical activity levels, and whether these associations differed between carriers and non-carriers of the apolipoprotein E (APOE) ɛ4 allele. Five-hundred and forty six cognitively intact participants (aged 60-95 years) from the Australian Imaging, Biomarkers and Lifestyle Study of Ageing (AIBL) were included in these analyses. Habitual physical activity levels were measured using the International Physical Activity Questionnaire (IPAQ). Serum insulin, glucose, cholesterol and plasma Aβ levels were measured in fasting blood samples. A subgroup (n=116) underwent (11)C-Pittsburgh compound B (PiB) positron emission tomography (PET) scanning to quantify brain amyloid load. Higher levels of physical activity were associated with higher high density lipoprotein (HDL) (P=0.037), and lower insulin (P<0.001), triglycerides (P=0.019) and Aβ(1-42/1-40) ratio (P=0.001). After stratification of the cohort based on APOE ɛ4 allele carriage, it was evident that only non-carriers received the benefit of reduced plasma Aβ from physical activity. Conversely, lower levels of PiB SUVR (standardised uptake value ratio) were observed in higher exercising APOE ɛ4 carriers. Lower plasma Aβ(1-42/1-40) and brain amyloid was observed in those reporting higher levels of physical activity, consistent with the hypothesis that physical activity may be involved in the modulation of pathogenic changes associated with AD.Molecular Psychiatry advance online publication, 14 August 2012; doi:10.1038/mp.2012.107.
Cognitive and physical decline are important predictors of functional independence in Alzheimer's disease (AD). However, little is known about AD-related neural change leading to decreased independence. We hypothesized that regional gray matter atrophy, including the medial frontal cortex, would be related to cognition, physical function, and functional independence. Individuals without dementia (n = 56) and subjects with early-stage AD (n = 58) underwent MRI and a comprehensive cognitive and physical function evaluation. The relationship of cognitive and physical function measures and independence performing complex daily activities was explored using correlation and mediation analysis. These results suggest that cognition had both a strong direct effect and mediated the influence of physical function on independence for those with AD. We followed this with a voxel-based morphometric global conjunction analysis of imaging data within each group to identify neural substrates common to our function measures. Imaging evidence supported our mediation analysis results. Imaging evidence revealed that in AD, regional gray matter atrophy measures in medial frontal and temporo-parietal areas were related to decreased cognition, physical function, and independence. Loss of independence in early AD is closely related to impaired cognition associated with performing complex behaviors. People with early AD may have decreased gray matter volume in the medial frontal and temporal-parietal cortices that is associated with loss of independence in activities of daily living. These results are the first to identify regionally specific brain volume changes that may be related to functional dependence seen in early AD.
Higher physical activity levels are associated with reduced cognitive decline among older adults; however, current understanding of underlying brain mechanisms is limited. This cross-sectional study investigated the relationship between actigraphy-estimated total volume of physical activity (TVPA) and magnetic resonance imaging (MRI) measures of white matter hyperintensities (WMH), and functional and structural brain connectivity, measured by resting-state functional MRI and diffusion tensor imaging. Study participants (N=156, mean age=71 years) included 136 with normal cognition and 20 with Mild Cognitive Impairment. Higher TVPA was associated with greater functional connectivity within the default-mode network and greater network modularity (a measure of network specialization), as well as with greater anisotropy and lower radial diffusion in white matter, suggesting better structural connectivity. These associations with functional and structural connectivity were independent of one another, and independent of level of vascular risk, APOE-e4 status, cognitive reserve, and WMH volume, which was not associated with TVPA. Findings suggests that physical activity is beneficial for brain connectivity among older individuals with varying levels of risk for cognitive decline.
The health benefits of physical activity have been widely recognized, yet traditional measures of physical activity including questionnaires and category-based assessments of volume and intensity provide only broad estimates of daily activities. Accelerometers have advanced epidemiologic research on physical activity by providing objective and continuous measurement of physical activity in free-living conditions. Wrist-worn accelerometers have become especially popular due to low participant burden. However, the validity and reliability of wrist-worn devices for adults have yet to be summarized. Moreover, accelerometer data provide rich information on how physical activity is accumulated throughout the day, but only a small portion of these rich data have been utilized by researchers. Lastly, new methodological developments that aim to overcome some of the limitations of accelerometers are emerging. The purpose of this review is to provide an overview of accelerometry research, with a special focus on wrist-worn accelerometers. We describe briefly how accelerometers work, summarize the validity and reliability of wrist-worn accelerometers, discuss the benefits of accelerometers including measuring light-intensity physical activity, and discuss pattern metrics of daily physical activity recently introduced in the literature. A summary of large-scale cohort studies and randomized trials that implemented wrist-worn accelerometry is provided. We conclude the review by discussing new developments and future directions of research using accelerometers, with a focus on wrist-worn accelerometers.
Background
Physical activity becomes increasingly fragmented with age, which may be an early marker of functional decline. Energetic cost of walking and energy capacity are also linked with functional decline, but their associations with activity fragmentation, and the potential modifying roles of total daily physical activity and age, remains unclear.
Methods
493 participants (50-93 years) from the Baltimore Longitudinal Study of Aging underwent measures of: energetic cost of usual-paced overground walking (ml/kg/m), energy demand during slow walking (ml/kg/min) on a treadmill (0.67 m/s, 0% grade),m/s, 0% grade), and average peak walking energy expenditure (ml/kg/min) during a fast-paced 400-m walk. A ratio of slow walking to peak walking energy expenditure (“cost-to-capacity ratio”) was calculated. Activity fragmentation was quantified as an active-to-sedentary transition probability (ASTP) using Actiheart accelerometer data. Linear regression models with ASTP as the dependent variable were used to test whether poorer energy cost and capacity were associated with higher ASTP and whether the associations differed by daily physical activity or age.
Results
After adjusting for demographics, body composition, comorbidities and daily physical activity, every 10% higher cost-to-capacity ratio was associated with 0.4% greater ASTP (p=0.005). This association was primarily driven by the least active participants (pinteraction=0.023). Peak walking energy expenditure was only associated with ASTP among participants aged ≥70 years.
Conclusions
Higher cost-to-capacity ratio and lower energy capacity may manifest as more fragmented physical activity, especially among those less active or aged ≥70 years. Future studies should examine whether an increasing cost-to-capacity ratio or declining energy capacity predicts subsequent activity fragmentation.
Background
Larger brain volumes are often associated with more free-living physical activity (PA) in cognitively normal older adults. Yet, whether greater brain volumes are associated with more favorable (less fragmented) PA patterns, and whether this association is stronger than with total PA, remains unknown.
Methods
Brain magnetic resonance imaging and wrist-worn accelerometer data were collected in 301 participants (mean age=77[SD=7] years, 59% women) enrolled in the Baltimore Longitudinal Study of Aging. Linear regression models were fit to examine whether brain volumes (cc) were cross-sectionally associated with: 1) total daily PA minutes; and 2) activity fragmentation (mean number of PA bouts / total PA minutes x 100). Sensitivity analyses were conducted by adjusting for counterpart PA variables (e.g., fragmentation covariate included in the PA minutes model).
Results
Greater white matter volumes in the parietal and temporal lobes were associated with higher daily PA minutes (2.6(SE=1.0) and 3.8(0.9)min/day, respectively; p<0.009 for both) after adjusting for demographics, behavioral factors, medical conditions, gait speed, apolipoprotein E e4 status, and intracranial volume. Greater temporal white matter volume was associated with lower fragmentation (-0.16(0.05)%, p=0.003). In sensitivity analyses, observed associations between brain volumes and daily PA minutes remained significant while associations with fragmentation no longer remained significant.
Conclusions
Our results suggest white matter brain structure in cognitively normal older adults is associated with the total amount of PA and, to a lesser extent, the PA accumulation patterns. More work is needed to elucidate the longitudinal relationship between brain structure and function and PA patterns with aging.
Importance
Although the most common recent approach in Alzheimer disease drug discovery is to directly target the β-amyloid (Aβ) pathway, the high prevalence of apolipoprotein E ε4 (APOE ε4) in Alzheimer disease and the ease of identifying ε4 carriers make the APOE genotype and its corresponding protein (apoE) an appealing therapeutic target to slow Aβ accumulation.
Objective
To determine whether the ε2 allele is protective against Aβ accumulation in the presence of the ε4 allele and evaluate how age and the APOE genotype are associated with emerging Aβ accumulation and cognitive dysfunction.
Design, Setting, and Participants
This cross-sectional study used screening data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease Study (A4 Study) collected from April 2014 to December 2017 and analyzed from November 2019 to July 2020. Of the 6943 participants who were a part of the multicenter clinical trial screening visit, 4432 were adults without cognitive impairment aged 65 to 85 years who completed a fluorine 18–labeled (18F)-florbetapir positron emission tomography scan, had APOE genotype information, and had a Clinical Dementia Rating of 0. Participants who were taking a prescription Alzheimer medication or had a current serious or unstable illness that could interfere with the study were excluded.
Main Outcomes and Measures
Aβ pathology, measured by 18F-florbetapir positron emission tomography and cognition, measured by the Preclinical Alzheimer Cognitive Composite.
Results
A total of 4432 participants were included (mean [SD] age, 71.3 [4.7] years; 2634 women [59.4%]), with a mean (SD) of 16.6 (2.8) years of education and 1512 (34.1%) with a positive Aβ level. APOE ε2 was associated with a reduction in both the overall (standardized uptake value ratio [SUVR], ε24, 1.11 [95% CI, 1.08-1.14]; ε34, 1.18 [95% CI, 1.17-1.19]) and the age-dependent level of Aβ in the presence of ε4, with Aβ levels in the APOE ε24 group (n = 115; ε24, 0.005 SUVR increase per year of age) increasing at less than half the rate with respect to increasing age compared with the APOE ε34 group (n = 1295; 0.012 SUVR increase per year of age; P = .04). The association between Aβ and decreasing Preclinical Alzheimer Cognitive Composite scores did not differ by APOE genotype, and the reduced performance on the Preclinical Alzheimer Cognitive Composite in APOE ε4 carriers compared with noncarriers was completely mediated by Aβ (unadjusted difference in composite scores between ε4 carriers and noncarriers = –0.084, P = .005; after adjusting for 18F-florbetapir = –0.006, P = .85; after adjusting for 18F-florbetapir and cardiovascular scores = –0.009, P = .78).
Conclusions and Relevance
These findings suggest that the protective outcome of carrying an ε2 allele in the presence of an ε4 allele against Aβ accumulation is important for potential treatments that attempt to biochemically mimic the function of the ε2 allele in order to facilitate Aβ clearance in ε4 carriers. Such a treatment strategy is appealing, as ε4 carriers make up approximately two-thirds of patients with Alzheimer disease dementia. This strategy could represent an early treatment option, as many ε4 carriers begin to accumulate Aβ in early middle age.
Over the past three decades, considerable effort has been dedicated to quantifying the pace of ageing yet identifying the most essential metrics of ageing remains challenging due to lack of comprehensive measurements and heterogeneity of the ageing processes. Most of the previously proposed metrics of ageing have been emerged from cross-sectional associations with chronological age and predictive accuracy of mortality, thus lacking a conceptual model of functional or phenotypic domains. Further, such models may be biased by selective attrition and are unable to address underlying biological constructs contributing to functional markers of age-related decline. Using longitudinal data from the Baltimore Longitudinal Study of Aging (BLSA), we propose a conceptual framework to identify metrics of ageing that may capture the hierarchical and temporal relationships between functional ageing, phenotypic ageing and biological ageing based on four hypothesized domains: body composition, energy regulation, homeostatic mechanisms and neurodegeneration/neuroplasticity. We explored the longitudinal trajectories of key variables within these phenotypes using linear mixed-effects models and more than 10 years of data. Understanding the longitudinal trajectories across these domains in the BLSA provides a reference for researchers, informs future refinement of the phenotypic ageing framework and establishes a solid foundation for future models of biological ageing.
Background
With aging, daily physical activity (PA) becomes less frequent and more fragmented. Accumulation patterns of daily PA -including transitions from active-to-sedentary behaviors- may provide important insights into functional status in older, less active populations.
Methods
Participants of the Baltimore Longitudinal Study of Aging (n = 680, 50% male, age 27–94) completed a clinical assessment and wore an Actiheart accelerometer. Transitions between active and sedentary states were modeled as a probability (Active-to-Sedentary Transition Probability (ASTP)) defined as the reciprocal of the average PA bout duration. Cross-sectional associations between ASTP and gait speed (m/s), fatigability (rating-of-perceived-exertion (RPE)), 400-meter time (seconds), and expanded short physical performance battery score (ExSPPB) were modeled using linear and logistic regression, adjusted for chronic conditions. Further analyses explored the utility of ASTP over-and-above total daily PA.
Results
In continuous models, each 0.10-unit higher ASTP was associated slower gait (=-0.06 m/s, SD=0.01), higher fatigability (=0.60 RPE, SD=0.12), slower 400-meter-time (=16.31 sec, SD=2.70), and lower functioning (=-0.13 ExSPPB, SD=0.03) (p< 0.001). In categorical analyses, those in the highest tertile of ASTP were >2-times more likely to have high fatigability (RPE ≥10), slow 400-meter time (>300 seconds) and reduced functional performance (ExSPPB <3.07) than those in the lowest tertile (p <0.01). Further analyses demonstrated ASTP provided additional insight into functional outcomes beyond total daily PA.
Conclusion
Fragmented daily PA -as measured by ASTP- is strongly linked with measures of health and functional status and may identify those at risk of high fatigability and reduced functional performance over-and-above traditional PA metrics.
Study Objectives
To determine the association of excessive daytime sleepiness (EDS) and napping with subsequent brain β-amyloid (Aβ) deposition in cognitively normal persons.
Methods
We studied 124 community-dwelling participants in the Baltimore Longitudinal Study of Aging Neuroimaging Substudy who completed self-report measures of EDS and napping at our study baseline and underwent [¹¹C] Pittsburgh compound B positron emission tomography (PiB PET) scans of the brain, an average ±standard deviation of 15.7 ± 3.4 years later (range 6.9 to 24.6). Scans with a cortical distribution volume ratio of >1.06 were considered Aβ-positive.
Results
Participants were aged 60.1 ± 9.8 years (range 36.2 to 82.7) at study baseline; 24.4% had EDS and 28.5% napped. In unadjusted analyses, compared with participants without EDS, those with EDS had more than 3 times the odds of being Aβ+ at follow-up (odds ratio [OR] = 3.37, 95% confidence interval [CI]: 1.44, 7.90, p = 0.005), and 2.75 times the odds after adjustment for age, age², sex, education, and body mass index (OR = 2.75, 95% CI: 1.09, 6.95, p = 0.033). There was a trend-level unadjusted association between napping and Aβ status (OR = 2.01, 95% CI: 0.90, 4.50, p = 0.091) that became nonsignificant after adjustment (OR = 1.86, 95% CI: 0.73, 4.75, p = 0.194).
Conclusions
EDS is associated with more than 2.5 times the odds of Aβ deposition an average of 15.7 years later. If common EDS causes (e.g., sleep-disordered breathing, insufficient sleep) are associated with temporally distal AD biomarkers, this could have important implications for AD prevention.
Health neuroscience is a new interdisciplinary field that combines theories and techniques from health psychology and cognitive and social–affective neuroscience in order to understand how the brain affects and is affected by health behaviors. Physical activity (PA) research can serve as a useful model for various ways in which the brain can be incorporated into health neuroscience studies to better understand variability in the adoption and maintenance of, as well as benefits gained from, health behaviors. Here, we summarize evidence linking PA to brain and cognitive performance from studies conceptualizing the brain as either an outcome or mediator of cognitive change. We then discuss an emerging body of studies using a brain as a predictor approach. We discuss how studies using this approach complement existing PA studies and provide insight into a major source of variability in the outcomes of PA interventions, above and beyond the variability accounted for by known biological and demographic moderators. A more complete understanding of the bidirectional relationships between brain and behaviors, such as PA, could provide valuable insight into how to tailor interventions to optimally affect individuals, identify key barriers, and inform the development of novel policies to promote public health. Physical activity (PA) research provides a model for incorporating the brain into health neuroscience studies to understand variability in the adoption and maintenance of, as well as benefits gained from, health behaviors. We summarize evidence linking PA to brain and cognitive performance from studies conceptualizing the brain as either an outcome or mediator of cognitive change.
Background
The longitudinal association between cerebral amyloid-beta (Aβ) and change in gait, and whether this association is mediated by cortical thickness, has yet to be determined.
Methods
We included 439 clinically normal (CN) participants, aged 50-69 years and enrolled in the Mayo Clinic Study of Aging with cerebral Aβ, cortical thickness, and gait measurements. Cerebral Aβ deposition was assessed by Pittsburgh Compound B (PiB)-PET in multiple regions of interest (ROIs) (i.e., frontal, orbitofrontal, parietal, temporal, anterior cingulate, posterior cingulate/precuneus, and motor). Cortical thickness was assessed on 3T MRI in corresponding ROIs. Gait parameters (gait speed, cadence, stride length, double support time, and covariance of stance time) were measured with GAITRite®. Multivariate-adjusted two level structural equation models were used to examine the longitudinal association between PiB-PET, cortical thickness, and change in gait over a median 15.6 months.
Results
Higher PiB-PET in all ROIs was associated with decreasing cadence and increasing double support time, and in the temporal ROI was associated with declining gait speed. In sex-stratified analyses, higher PiB-PET in all ROIs was associated with declining performance on all gait parameters among women. In contrast, among men, the only association was with higher orbitofrontal ROI PiB-PET and declining cadence. None of the associations were mediated by cortical thickness or attenuated after adjustment of baseline cognition.
Conclusion
Higher PiB-PET was associated with declining gait, particularly among women in this middle-aged CN cohort, independent of cortical thickness and baseline cognitive. Elevated brain Aβ may play a critical role in age-related mobility decline.
Objectives:
Careful characterization of how functional decline co-evolves with cognitive decline in older adults has yet to be well described. Most models of neurodegenerative disease postulate that cognitive decline predates and potentially leads to declines in everyday functional abilities; however, there is mounting evidence that subtle decline in instrumental activities of daily living (IADLs) may be detectable in older individuals who are still cognitively normal.
Methods:
The present study examines how the relationship between change in cognition and change in IADLs are best characterized among older adults who participated in the ACTIVE trial. Neuropsychological and IADL data were analyzed for 2802 older adults who were cognitively normal at study baseline and followed for up to 10 years.
Results:
Findings demonstrate that subtle, self-perceived difficulties in performing IADLs preceded and predicted subsequent declines on cognitive tests of memory, reasoning, and speed of processing.
Conclusions:
Findings are consistent with a growing body of literature suggesting that subjective changes in everyday abilities can be associated with more precipitous decline on objective cognitive measures and the development of mild cognitive impairment and dementia. (JINS, 2017, 23, 1-9).
Introduction:
The objective of this study was to evaluate the relationship between self-reported exercise levels and Alzheimer's disease (AD) biomarkers, in a cohort of autosomal dominant AD mutation carriers.
Methods:
In 139 presymptomatic mutation carriers from the Dominantly Inherited Alzheimer Network, the relationship between self-reported exercise levels and brain amyloid load, cerebrospinal fluid (CSF) Aβ42, and CSF tau levels was evaluated using linear regression.
Results:
No differences in brain amyloid load, CSF Aβ42, or CSF tau were observed between low and high exercise groups. Nevertheless, when examining only those already accumulating AD pathology (i.e., amyloid positive), low exercisers had higher mean levels of brain amyloid than high exercisers. Furthermore, the interaction between exercise and estimated years from expected symptom onset was a significant predictor of brain amyloid levels.
Discussion:
Our findings indicate a relationship exists between self-reported exercise levels and brain amyloid in autosomal dominant AD mutation carriers.
Objectives:
To determine the cross-sectional association between cerebral amyloid-beta (Aβ) deposition and gait.
Design:
Cross-sectional.
Setting:
Population-based cohort study in Olmsted County, MN.
Participants:
Cognitively normal individuals (n = 611), aged 50 to 69 years, enrolled in the Mayo Clinic Study of Aging with concurrent PiB-PET imaging and gait assessment. Participants with a history of stroke, alcoholism, Parkinson's disease, subdural hematoma, traumatic brain injury, or normal pressure hydrocephalus were excluded.
Measurements:
PiB-PET SUVR was measured in prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, posterior cingulate, and motor-specific regions of interest (ROIs). Gait parameters (speed, cadence, stride length, double support time, and intra-individual stance time variability) were measured using GAITRite(®) instrumentation. Linear regression models were adjusted for age, sex, body mass index, education, APOE ε4 allele, Charlson comorbidity index, and depression. In secondary analyses, we additionally adjusted for neurodegeneration (hippocampal volume, FDG PET SUVR, and cortical thickness) in AD-associated regions.
Results:
In fully adjusted models including neuroimaging measures of neurodegeneration, higher PiB-PET SUVR across all ROIs was associated with slower gait speed (P < .05 except for the parietal ROI), lower cadence and longer double support time (P ≤ .05 except for the motor ROI), and greater stance time variability (P < .05). In sex-stratified analyses, the association between higher PiB-PET SUVR across all ROIs and measures of gait was only present among women.
Conclusion:
PiB-PET SUVR across ROIs, independent of general measures of AD-associated neurodegeneration, is associated with poorer performance on multiple gait parameters among cognitively normal women, aged 50 to 69 years. Longitudinal studies are needed to determine whether Aβ predicts gait decline in both women and men.
Background. Motor slowing is associated with risk of Alzheimer’s disease (AD). Whether β-amyloid (Aβ) burden is associated with motor decline, independent of cognitive decline, is unknown.
Methods. 59 cognitively unimpaired older participants had baseline PET-PiB scans and repeated measures of lower (usual gait speed, 400m time, Health ABC Physical Performance Battery (HABCPPB) score, total standing balance time) and upper (mean tapping time) extremity performance during a mean follow-up of 4.7 years. Linear mixed effect models examined the relationship between baseline Aβ burden and motor decline, adjusting for age, sex, body mass index, cardiovascular risk, APOE ɛ4 status, memory decline, depressive symptoms, ankle-arm index, processing speed, executive function, and cerebrovascular disease.
Results. Higher mean cortical Aβ burden was associated with greater declines in gait speed and HABCPPB score, and a greater increase in 400m time. Higher Aβ of putamen was associated with declines in all lower extremity measures, including balance. Higher Aβ of dorsolateral prefrontal cortex and lateral temporal lobe was associated with declines of gait speed and 400m time, and of precuneus with a greater increase in 400m time. Associations remained similar after further adjustment.
Conclusions. In cognitively unimpaired older adults, Aβ burden overall and in specific brain regions are risk factors for lower extremity motor decline, independent of memory function. These findings provide the first empirical evidence that Aβ burden is a risk factor for mobility decline in older adults.
Purpose:
Sedentary behavior is an emerging independent health risk factor. The accuracy of measuring sedentary time using accelerometers may depend on the wear location. This study in older adults evaluated the accuracy of various hip- and wrist-worn ActiGraph accelerometer cut-off points to define sedentary time using the activPAL as the reference method.
Methods:
Data from 62 adults (mean age 78.4 years) of the Aging Research Evaluating Accelerometry (AREA) study were used. Participants simultaneously wore an activPAL accelerometer on the thigh and ActiGraph accelerometers on the hip, dominant, and nondominant wrist for 7 days in a free-living environment. Using the activPAL as the reference criteria, we compared classification of sedentary time to hip-worn and wrist-worn ActiGraph accelerometers over a range of cut-off points for both 60-second and 15-second epochs.
Results:
The optimal cut-off point for the hip vertical axis was <22 counts per minute (cpm) with an area under the curve (AUC) of 0.85; the optimal hip vector magnitude cut-off point was <174 cpm with an AUC of 0.89. For the dominant wrist, the optimal vector magnitude cut-off point to define sedentary time was <2303 cpm (AUC: 0.86) and for the nondominant wrist <1853 cpm (AUC: 0.86). The optimal 15s cut-off points results in lower agreements compared with activPAL.
Conclusions:
Hip- and wrist-worn ActiGraph data may be used to define sedentary time with a moderate to high accuracy when compared to activPAL. The observed optimal cut-off point for hip vertical axis <22 cpm is substantially lower than the standard <100 cpm. It is unknown how these optimal cut-off points perform in different populations. Results on an individual basis should therefore be interpreted with caution.
The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depression which have been used in previously validated longer scales. The new scale was tested in household interview surveys and in psychiatric settings. It was found to have very high internal consistency and adequate test- retest repeatability. Validity was established by pat terns of correlations with other self-report measures, by correlations with clinical ratings of depression, and by relationships with other variables which support its construct validity. Reliability, validity, and factor structure were similar across a wide variety of demographic characteristics in the general population samples tested. The scale should be a useful tool for epidemiologic studies of de pression.
Objective APOE ϵ4 status has been associated with greater cortical amyloid deposition, whereas exercise has been associated with less in cognitively normal adults. The primary objective here was to examine whether physical exercise moderates the association between APOE genotype and amyloid deposition in cognitively normal adults.
Design APOE genotyping data and answers to a questionnaire on physical exercise engagement over the last decade were obtained in conjunction with cerebrospinal fluid (CSF) samples and amyloid imaging with carbon 11–labeled Pittsburgh Compound B ([11C]PiB) positron emission tomography. Participants were classified as either low or high exercisers based on exercise guidelines of the American Heart Association.
Setting Knight Alzheimer's Disease Research Center at Washington University, St Louis, Missouri.
Participants A total of 201 cognitively normal adults (135 of whom were women) aged 45 to 88 years were recruited from the Knight Alzheimer’s Disease Research Center. Samples of CSF were collected from 165 participants. Amyloid imaging was performed for 163 participants.
Results APOE ϵ4 carriers evidenced higher [11C]PiB binding (P < .001) and lower CSF Aβ42 levels (P < .001) than did noncarriers. Our previous findings of higher [11C]PiB binding (P = .005) and lower CSF Aβ42 levels (P = .009) in more sedentary individuals were replicated. Most importantly, we observed a novel interaction between APOE status and exercise engagement for [11C]PiB binding (P = .008) such that a more sedentary lifestyle was significantly associated with higher [11C]PiB binding for ϵ4 carriers (P = .013) but not for noncarriers (P = .20). All findings remained significant after controlling for age; sex; educational level; body mass index; the presence or history of hypertension, diabetes mellitus, heart problems, or depression; and the interval between assessments.
Conclusion Collectively, these results suggest that cognitively normal sedentary APOE ϵ4–positive individuals may be at augmented risk for cerebral amyloid deposition.
In addition to the increasingly recognized role of physical exercise in maintaining cognition, exercise may influence Alzheimer's disease (AD) pathology, as transgenic mouse studies show lowered levels of AD pathology in exercise groups. The objective of this study was to elucidate the association between exercise and AD pathology in humans using Pittsburgh compound-B (PIB), amyloid-beta (Abeta)(42), tau, and phosphorylated tau (ptau)(181) biomarkers.
Sixty-nine older adults (17 males, 52 females) aged 55 to 88 years, were recruited and confirmed to be cognitively normal. A questionnaire on physical exercise levels over the past decade was administered to all. Cerebrospinal fluid samples were collected from 56 participants, and amyloid imaging with PIB was performed on 54 participants.
Participants were classified based on biomarker levels. Those with elevated PIB (p = 0.030), tau (p = 0.040), and ptau(181) (p = 0.044) had significantly lower exercise, with a nonsignificant trend for lower Abeta(42) (p = 0.135) to be associated with less exercise. Results were similar for PIB after controlling for covariates; tau (p = 0.115) and ptau(181) (p = 0.123) differences were reduced to nonsignificant trends. Additional analyses also demonstrated that active individuals who met the exercise guidelines set by the American Heart Association had significantly lower PIB binding and higher Abeta(42) levels with and without controlling for covariates (PIB: p = 0.006 and p = 0.001; Abeta(42): p = 0.042 and p = 0.046). Last, the associations between exercise engagement and PIB levels were more prominent in APOE epsilon 4 noncarriers.
Collectively, these results are supportive of an association between exercise engagement and AD biomarkers in cognitively normal older adults.
the use of movement monitors (accelerometers) for measuring physical activity (PA) in intervention and population-based studies is becoming a standard methodology for the objective measurement of sedentary and active behaviors and for the validation of subjective PA self-reports. A vital step in PA measurement is the classification of daily time into accelerometer wear and nonwear intervals using its recordings (counts) and an accelerometer-specific algorithm.
the purpose of this study was to validate and improve a commonly used algorithm for classifying accelerometer wear and nonwear time intervals using objective movement data obtained in the whole-room indirect calorimeter.
we conducted a validation study of a wear or nonwear automatic algorithm using data obtained from 49 adults and 76 youth wearing accelerometers during a strictly monitored 24-h stay in a room calorimeter. The accelerometer wear and nonwear time classified by the algorithm was compared with actual wearing time. Potential improvements to the algorithm were examined using the minimum classification error as an optimization target.
the recommended elements in the new algorithm are as follows: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero or nonzero counts, and 3) allowance of 2-min interval of nonzero counts with the upstream or downstream 30-min consecutive zero-count window for detection of artifactual movements. Compared with the true wearing status, improvements to the algorithm decreased nonwear time misclassification during the waking and the 24-h periods (all P values < 0.001).
the accelerometer wear or nonwear time algorithm improvements may lead to more accurate estimation of time spent in sedentary and active behaviors.
We carried out a correlational analysis of the scores on the Blessed Information-Memory-Concentration Test and the Mini-Mental State Exam in 40 subjects with Alzheimer's disease. The average product moment correlation coefficient between the two tests on repeated administration over 6 weeks ranged from -0.73 to -0.83. Both tests demonstrated test-retest reliability coefficients of 0.75 and above. A formula was developed to convert one test score to the other.
The rising number of people with cognitive impairment is placing health care budgets under significant strain. Dementia related behavioural change is a major independent risk factor for admission to expensive institutional care, and aggressive symptoms in particular are poorly tolerated by carers and frequently precipitate the collapse of home coping strategies. Aggressive change may result from known genetic risk factors for Alzheimer's disease (AD) and therefore accompany conventional markers such as apolipoprotein E (ApoE). We tested this hypothesis in 400 moderately to severely affected AD patients who were phenotyped for the presence of aggressive or agitated behaviour during the month prior to interview using the Neuropsychiatric Inventory with Caregiver Distress. The proportion of subjects with aggression/agitation in the month prior to interview was 51.8%. A significantly higher frequency of the e4 allele was found in individuals recording aggression/agitation in the month prior to interview (chi2 = 6.69, df = 2, p = 0.03). The additional risk for aggression/agitation conferred by e4 was also noted when e4 genotypes were compared against non-e4 genotypes (chi2 = 5.45, df = 1, p = 0.02, OR = 1.60, confidence interval (CI) 1.06 to 2.43). These results indicate that advanced Alzheimer's disease patients are at greater risk of aggressive symptoms because of a genetic weakness in apolipoprotein E.