Objectives:Patients with essential tremor exhibit heterogeneous cognitive functioning. Although the majority of patients fall under the broad classification of cognitively “normal,” essential tremor is associated with increased risk for mild cognitive impairment and dementia. It is possible that patterns of cognitive performance within the wide range of normal functioning have predictive utility for mild cognitive impairment or dementia. These cross-sectional analyses sought to determine whether cognitive patterns, or “clusters,” could be identified among individuals with essential tremor diagnosed as cognitively normal. We also determined whether such clusters, if identified, were associated with demographic or clinical characteristics of patients.Methods:Elderly subjects with essential tremor (age >55 years) underwent comprehensive neuropsychological testing. Domain means (memory, executive function, attention, visuospatial abilities, and language) from 148 individuals diagnosed as cognitively normal were partitioned using k-means cluster analysis. Individuals in each cluster were compared according to cognitive functioning (domain means and test scores), demographic factors, and clinical variables.Results:There were three clusters. Cluster 1 (n = 64) was characterized by comparatively low memory scores (p < .001), Cluster 2 (n = 39) had relatively low attention and visuospatial scores (p < .001), and Cluster 3 (n = 45) exhibited consistently high performance across all domains. Cluster 1 had lower Montreal Cognitive Assessment scores and reported more prescription medication use and lower balance confidence.Conclusions:Three patterns of cognitive functioning within the normal range were evident and tracked with certain clinical features. Future work will examine the extent to which such patterns predict conversion to mild cognitive impairment and/or dementia.

Objective: Essential tremor (ET) is among the most common neurologic diseases. Although in the past it was considered a benign condition, recent research has demonstrated increased risk of mortality. To date, however, no studies have examined predictors of mortality in ET.Methods: In a longitudinal, prospective study of 141 elders with ET, we used Cox proportional-hazards models to estimate hazard ratios (HRs) for death.Results: The mean baseline age was 81.1 ± 8.8 years. During the follow-up interval, 27 (19.1%) died. Average time from baseline to death was 12.3 ± 8.7 months (range = 0.3–31.2). In univariate Cox regression models, older age (HR = 1.16, p < 0.001), lower Montreal Cognitive Assessment score (HR = 0.88, p = 0.004), higher Clinical Dementia Rating (CDR) score (HR = 4.53, p < 0.001), higher score on the Geriatric Depression scale (GDS) (HR = 1.07, p = 0.048), less balance confidence (HR = 0.98, p = 0.006), more falls (HR = 1.11, p = 0.003), and more tandem mis-steps (HR = 1.53, p = 0.004) were associated with increased risk of mortality. In the final multivariate Cox model, older age (HR = 1.14, p = 0.005), higher CDR score (HR = 3.80, p = 0.002) and higher GDS (HR = 1.11, p = 0.01) were independently associated with increased risk of mortality.Conclusions: This study highlights several independent predictors of mortality in elderly ET; clinicians should consider screening for depressive symptoms, assessing cognition and tracking CDR scores, and assessing balance while evaluating patients with ET.

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Objective: Dementia is a devastating neurological disease that may be better managed if diagnosed earlier when subclinical neurodegenerative changes are already present, including subtle cognitive decline and mild cognitive impairment. In this study, we used item-level performance on the Montreal Cognitive Assessment (MoCA) to identify individuals with subtle cognitive decline. Method: Individual MoCA item data from the Alzheimer’s Disease Neuroimaging Initiative was grouped using k-modes cluster analysis. These clusters were validated and examined for association with convergent neuropsychological tests. The clusters were then compared and characterized using multinomial logistic regression. Results: A three-cluster solution had 77.3% precision, with Cluster 1 (high performing) displaying no deficits in performance, Cluster 2 (memory deficits) displaying lower memory performance, and Cluster 3 (compound deficits) displaying lower performance on memory and executive function. Age at MoCA (older in compound deficits), gender (more females in memory deficits), and marital status (fewer married in compound deficits) were significantly different among clusters. Age was not associated with increased odds of membership in the high-performing cluster compared to the others. Conclusions: We identified three clusters of individuals classified as cognitively unimpaired using cluster analysis. Individuals in the compound deficits cluster performed lower on the MoCA and were older and less often married than individuals in other clusters. Demographic analyses suggest that cluster identity was due to a combination of both cognitive and clinical factors. Identifying individuals at risk for future cognitive decline using the MoCA could help them receive earlier evidence-based interventions to slow further cognitive decline.

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