Determining the Minimal Important Change of Everyday Functioning in Dementia: Pursuing Clinical Meaningfulness.

BACKGROUND AND OBJECTIVES: Decline in everyday functioning is a key clinical change in Alzheimer disease and related disorders (ADRD). An important challenge remains the determination of what constitutes a clinically meaningful change in everyday functioning. We aimed to investigate this by establishing the minimal important change (MIC): the smallest amount of change that has a meaningful effect on patients' lives. We retrospectively investigated meaningful change in a memory clinic cohort. METHODS: In the first, qualitative part of the study, community-recruited informal caregivers of patients with ADRD and memory clinic clinicians completed a survey in which they judged various situations representing changes in everyday functioning. Their judgments of meaningful change were used to determine thresholds for MIC, both for decline and improvement, on the Amsterdam Instrumental Activities of Daily Living (IADL) Questionnaire. In the second, quantitative part, we applied these values in an independent longitudinal cohort study of unselected memory clinic patients. RESULTS: MIC thresholds were established at the average threshold of caregivers (N = 1,629; 62.4 ± 9.5 years; 77% female) and clinicians (N = 13): -2.2 points for clinically meaningful decline and +5.0 points for clinically meaningful improvement. Memory clinic patients (N = 230; 64.3 ± 7.7 years; 39% female; 60% dementia diagnosis) were followed for 1 year, 102 (45%) of whom showed a decline larger than the MIC, after a mean of 6.7 ± 3.5 months. Patients with a dementia diagnosis and more atrophy of the medial temporal lobe had larger odds (odds ratio [OR] = 3.4, 95% CI [1.5-7.8] and OR = 5.0, 95% CI [1.2-20.0], respectively) for passing the MIC threshold for decline than those with subjective cognitive complaints and no atrophy. DISCUSSION: We were able to operationalize clinically meaningful decline in IADL by determining the MIC. The usefulness of the MIC was supported by our findings from the clinical sample that nearly half of a sample of unselected memory clinic patients showed a meaningful decline in less than a year. Disease stage and medial temporal atrophy were predictors of functional decline greater than the MIC. Our findings provide guidance in interpreting changes in IADL and may help evaluate treatment effects and monitor disease progression.

Spatial-Temporal Patterns of ß-Amyloid Accumulation: A Subtype and Stage Inference Model Analysis.

BACKGROUND AND OBJECTIVES: ß-amyloid (Aß) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aß accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS: Amyloid-PET data of 3,010 participants were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion and the most probable subtype/stage classification per scan. The effects of demographics and risk factors on subtype assignment were assessed using multinomial logistic regression. RESULTS: Participants were mostly cognitively unimpaired (n = 1890 [62.8%]), had a mean age of 68.72 (SD 9.1) years, 42.1% were APOE ε4 carriers, and 51.8% were female. A 1-subtype model recovered the traditional amyloid accumulation trajectory, but SuStaIn identified 3 optimal subtypes, referred to as frontal, parietal, and occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to frontal (n = 415 [52.5%]), followed by parietal (n = 199 [25.3%]) and occipital subtypes (n = 175 [22.2%]). Significant differences across subtypes included distinct proportions of APOE ε4 carriers (frontal 61.8%, parietal 57.1%, occipital 49.4%), participants with dementia (frontal 19.7%, parietal 19.1%, occipital 31.0%), and lower age for the parietal subtype (frontal/occipital 72.1 years, parietal 69.3 years). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the frontal subtype; parietal and occipital subtypes did not differ. At follow-up, most participants (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION: Whereas a 1-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that 3 subtypes were optimal, showing distinct associations with Alzheimer disease risk factors. Further analyses to determine clinical utility are warranted.

Association of Education and Intracranial Volume With Cognitive Trajectories and Mortality Rates Across the Alzheimer Disease Continuum.

OBJECTIVE: To investigate relationships of education and intracranial volume (ICV) (factors related to cognitive and brain reserve, respectively) with cognitive trajectories and mortality in individuals with biomarker-defined Alzheimer disease (AD). METHODS: We selected 1,298 ß-amyloid-positive memory clinic patients with subjective cognitive decline (SCD, n = 142), mild cognitive impairment (MCI, n = 274), or AD dementia (n = 882) from the Amsterdam Dementia Cohort. All participants underwent baseline MRI and neuropsychological assessment, and 68% received cognitive follow-up (median 2.3 years, interquartile range 2.4). Mortality data were collected from the Central Public Administration. In the total sample and stratified by disease stage (i.e., SCD/MCI vs dementia), we examined education and ICV as predictors of baseline and longitudinal cognitive performance on 5 cognitive domains (memory, attention, executive, language, and visuospatial functions; linear mixed models) and time to death (Cox proportional hazard models). Analyses were adjusted for age, sex, whole brain gray matter atrophy, and MRI field strength. RESULTS: Education and ICV showed consistent positive associations with baseline cognition across disease stages. Longitudinally, we observed a relationship between higher education and faster cognitive decline among patients with dementia on global cognition, memory, executive function, and language (range ß = -0.06 to -0.13; all p < 0.05). Furthermore, in the total sample, both higher education and larger ICV were related to lower mortality risk (hazard ratio 0.84 and 0.82, respectively; p < 0.05). DISCUSSION: In this ß-amyloid-positive memory clinic sample, both cognitive and brain reserve were positively associated with baseline cognition, whereas only education was related to longitudinal cognition (i.e., accelerated decline among more highly educated patients with dementia). Higher education and ICV both moderately attenuated overall mortality risk in AD.

Cognitive and functional progression of dementia in two longitudinal studies.

OBJECTIVES: Previous studies have identified several subgroups (ie, latent trajectories) with distinct disease progression among people with dementia. However, the methods and results were not always consistent. This study aims to perform a coordinated analysis of latent trajectories of cognitive and functional progression in dementia across two datasets. METHODS: Included and analyzed using the same statistical approach were 1628 participants with dementia from the US National Alzheimer's Coordinating Center (NACC) and 331 participants with dementia from the Dutch Clinical Course of Cognition and Comorbidity study (4C-Study). Trajectories of cognition and instrumental activities of daily living (IADL) were modeled jointly in a parallel-process growth mixture model. RESULTS: Cognition and IADL tended to decline in unison across the two samples. Slow decline in both domains was observed in 26% of the US sample and 74% of the Dutch sample. Rapid decline in cognition and IADL was observed in 7% of the US sample and 26% of the Dutch sample. The majority (67%) of the US sample showed moderate cognitive decline and rapid IADL decline. CONCLUSIONS: Trajectories of slow and rapid dementia progression were identified in both samples. Despite using the same statistical methods, the number of latent trajectories was not replicated and the relative class sizes differed considerably across datasets. These results call for careful consideration when comparing progression estimates in the literature. In addition, the observed discrepancy between cognitive and functional decline stresses the need to monitor dementia progression across multiple domains.