Citations

AbstractBackgroundThe numeric clinical staging scheme described by the NIA‐AA workgroup proposes evidence of subtle decline on longitudinal cognitive testing as one option to define transitional cognitive decline for individuals in the Alzheimer’s continuum. Few studies have compared longitudinal and cross‐sectional definitions of subtle objective cognitive impairment (sOBJ). The study aim was to compare the diagnostic accuracy of cross‐sectional sOBJ and longitudinal objective cognitive decline (ΔOBJ) for preclinical Alzheimer’s disease (AD) based on Cogstate One Card Learning (OCL) performance.MethodWe included Mayo Clinic Study of Aging cognitively unimpaired (CU) participants who were A+T+ by amyloid and tau PET assessment (n=32) and A‐T‐ (n=211); all had at least two follow‐up visits. sOBJ was defined as performance ≤ ‐1SD on OCL accuracy using age‐corrected normative data. ΔOBJ was measured using two different methods: (1) within subjects’ standard deviation (WSD) using a ≤ ‐1 z‐score for change to define decline based on Cogstate’s normative change data, and (2) extracted subject‐specific slopes from a linear mixed effects model (LME) which can be interpreted as approximate annual change; decline was defined as slope < 10%ile among a reference sample of CU participants aged 50‐65 at baseline (N=732).ResultssOBJ and ΔOBJ‐WSD both showed low sensitivity in detection of A+T+ (9.4% and 12.5%, respectively). Total AUC values did not differ significantly (p=.31) across sOBJ (AUC = .64, CI = .53‐.75) and ΔOBJ‐WSD (AUC = .53, CI = .42‐.65) methods. Sensitivity of the ΔOBJ‐LME method to predict A+T+ was improved (37.5%) when using a conventional cutoff of <10%ile slope and was further improved (56%) when using an optimal derived cutoff equivalent to <17%ile slope within the reference group. Like the sOBJ method, the ΔOBJ‐LME method differentiated groups better than chance (AUC = .69, CI = .58‐.80). Despite increased sensitivity, total AUC values were not significantly different across sOBJ and ΔOBJ‐LME methods (p=.34). However, total AUC of ΔOBJ‐LME was significantly better than ΔOBJ‐WSD (p=.03).ConclusionsΔOBJ may be more sensitive to preclinical AD than sOBJ and may serve as a helpful method for identifying at risk CU individuals if advanced statistical methods are used for defining change.

AbstractBackgroundThe NIA‐AA Research Framework for Alzheimer’s disease (AD) includes consideration of two clinical schemes for staging AD: (1) traditional cognitively unimpaired (CU) to MCI to dementia and (2) a new numeric staging (1‐6) for individuals who are on the Alzheimer’s continuum.MethodThe Mayo Clinic Study of Aging is a population‐based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We analyzed CU persons who had completed three consecutive clinical visits and calculated measures for cross‐sectional categories: objective cognitive impairment and function and measures for change including subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (NBS). We calculated the frequencies for Stage 2 by varying the cutoffs for each of the measures above and assessed stability of this stage over 15 months.ResultThe frequency of Stage 2 varied from 5‐48% depending upon the age of the participants and the cutoff scores used. Membership in Stage 2 required abnormal SCD, ΔOBJ, or ΔNBS and having only ΔOBJ abnormal was the primary reason for inclusion in Stage 2 (45‐62%) followed by 10‐21% having only SCD abnormal. The stability of Stage 2 over 15 months ranged from 31‐65% with 5‐13% progressing to Stage 3 or higher and 22‐43% percent reverting to Stage 1.ConclusionStage 2 was characterized as a transitional period between CU and MCI and could be operationalized with a variety of measures. The staging scheme performed well considering the variables evaluated and cutoff scores employed.

AbstractBackgroundWhite matter changes and Alzheimer’s disease pathology frequently co‐exist in the population. Recently we empirically derived subtypes of incident mild cognitive impairment (MCI) and found that there was significant heterogeneity not only in cognitive impairment profiles but also gray matter atrophy suggesting that multiple etiologies may contribute to cognitive impairment in these subtypes. The aim of this study was to examine differences in white matter hyperintensity (WMH) volume in these same MCI subtypes and investigate amyloid deposition in a smaller subset.MethodWe analyzed data from 188 participants with incident MCI enrolled in the population‐based Mayo Clinic Study of Aging (MCSA) identified as having one of four MCI subtypes: subtle cognitive impairment (SCI), amnestic, dysnomic, and dysexecutive. All participants completed 3T imaging at the time of the MCI diagnosis. Using WMH volume scaled by TIV and PIB SUVR, we compared each MCI subtype to 344 cognitively impaired (CU) participants (mean age 78; 46% women) and each other.ResultThe amnestic (n = 83; mean age 80; 43% women), dysnomic (n = 30; mean age 84; 47% women) and dysexecutive subtypes (n = 49; mean age 83; 35% women) had significantly higher WMH volumes compared to CU (p < .02) while the SCI subtype (n = 26; mean age 80; 23% women) did not differ from CU (p = .76). Amongst the MCI subtypes, the amnestic (p = .04) and dysexecutive subtypes (p = .01) had significantly higher WMH volumes than the SCI subtype [Figure 1]. In a smaller subset of individuals who also completed PiB‐PET (17/26 SCI; 39/83 amnestic; 10/30 dysnomic, 28/49 dysexecutive), we found evidence for higher amyloid load in all MCI subgroups except the SCI group (p<0.05) in comparison to CU.ConclusionWe found significantly elevated levels of WMH and amyloid burden in amnestic, dysnomic, and dysexecutive subtypes of incident MCI supporting the notion that both vascular changes and amyloid contribute to the development of clinical symptoms. Surprisingly, WMH and amyloid positivity were not high in SCI suggesting that the pathophysiology in this group needs further investigation.

Background: Little is known about the association between physical activity (PA) and cognitive trajectories in older adults. Objective: To examine the association between PA and change in memory, language, attention, visuospatial skills, and global cognition, and a potential impact of sex or Apolipoprotein E (APOE) ɛ4 status. Methods: Longitudinal study derived from the population-based Mayo Clinic Study of Aging, including 2,060 cognitively unimpaired males and females aged ≥70 years. Engagement in midlife (ages 50–65) and late-life (last year) PA was assessed using a questionnaire. Neuropsychological testing was done every 15 months (mean follow-up 5.8 years). We ran linear mixed-effect models to examine whether mid- or late-life PA at three intensities (mild, moderate, vigorous) was associated with cognitive z-scores. Results: Light intensity midlife PA was associated with less decline in memory function compared to the no-PA reference group (time x light PA; estimate [standard error] 0.047 [0.016], p = 0.004). Vigorous late-life PA was associated with less decline in language (0.033 [0.015], p = 0.030), attention (0.032 [0.017], p = 0.050), and global cognition (0.039 [0.016], p = 0.012). Females who were physically inactive in midlife experienced more pronounced cognitive decline than females physically active in midlife and males regardless of PA (p-values for time interaction terms with midlife PA levels and sex were all p < 0.05 for global cognition). APOE ɛ4 carriership did not moderate the association between PA and cognition. Conclusion: Engaging in PA, particularly of vigorous intensity in late-life, was associated with less pronounced decline in global and domain-specific cognition. This association may differ by sex.

Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.

ObjectiveThe aim was to analyze the timeline, prevalence, and survival of rapid eye movement (REM) sleep behavior disorder (RBD) in patients who developed alpha‐synucleinopathies (Parkinson disease, dementia with Lewy bodies, and Parkinson disease dementia) compared with age‐ and sex‐matched controls in a population‐based incident‐cohort study.MethodsWe used a population‐based, 1991 to 2010 incident‐cohort study of alpha‐synucleinopathies. A movement‐disorder specialist reviewed medical records to confirm diagnoses. RBD was diagnosed by reported dream‐enactment symptoms or polysomnography. Probable RBD and polysomnographically confirmed RBD were analyzed separately and combined.ResultsAmong the 444 incident cases of alpha‐synucleinopathy, 86 were clinically diagnosed with RBD (19.8%), including 30 (35%) by polysomnography and 56 (65%) as probable. The prevalence of idiopathic RBD at alpha‐synucleinopathy diagnosis was 3.4%, increasing to 23.8% after 15 years. Cumulative lifetime incidence was 53 times greater in alpha‐synucleinopathy patients than in controls (odds ratio [OR] = 53.1, 95% confidence interval [CI]: 13.0–217.2, p < 0.0001), higher in dementia with Lewy bodies than in Parkinson disease (OR = 2.57, 95% CI: 1.50–4.40, p = 0.0004), and higher in men than in women with Parkinson disease, dementia with Lewy bodies, or Parkinson disease dementia (OR = 3.70, 95% CI: 2.07–6.62, p < 0.0001), but did not increase mortality risk.InterpretationOur cohort had RBD incidence of 3.4%. Overall RBD increased to 23.8% after 15 years, with an overall incidence of 2.5 cases per 100 person‐years. With 53 times greater lifetime incidence in alpha‐synucleinopathy patients than in controls, RBD was more likely to develop in dementia with Lewy bodies than in Parkinson disease or Parkinson disease dementia, and in men than in women, but did not increase mortality risk within our cohort. ANN NEUROL 2021;89:293–303

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.