Trajectories of cognitive decline and dementia development: A 12-year longitudinal study.

INTRODUCTION: Mapping the preclinical dementia phase is important for early detection and evaluation of interventions. We assessed the trajectories of cognitive decline in preclinical dementia over 12 years and investigated whether being a fast decliner across 6 years is associated with increased risk of dementia the following 6 years. METHODS: Rates of cognitive decline were determined using mixed-effects models for 1646 participants from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) cohort. Cox regression was used to assess the future likelihood of dementia for fast decliners (declining ≥1.5 standard deviations [SDs] faster than the age-specific rates). RESULTS: Participants in a preclinical phase of dementia showed increased rates of decline in all cognitive tests compared to the no-dementia group, particularly closer (0-6 years) to diagnosis. Participants declining fast in three or more cognitive tests 12-6 years before diagnosis demonstrated a high risk of dementia 6 years later (hazard ratio [HR] 3.90, 95% confidence interval [CI] 2.28-6.69). DISCUSSION: Being a fast decliner is linked to increased risk of future dementia.

Cognitive, Genetic, Brain Volume, and Diffusion Tensor Imaging Markers as Early Indicators of Dementia.

BACKGROUND: Although associated with dementia and cognitive impairment, microstructural white matter integrity is a rarely used marker of preclinical dementia. OBJECTIVE: We aimed to evaluate the individual and combined effects of multiple markers, with special focus on microstructural white matter integrity, in detecting individuals with increased dementia risk. METHODS: A dementia-free subsample (n = 212, mean age = 71.33 years) included in the population-based Swedish National Study on Aging and Care (SNAC-K) underwent magnetic resonance imaging (T1-weighted, fluid-attenuated inversion recovery, diffusion tensor imaging), neuropsychological testing (perceptual speed, episodic memory, semantic memory, letter and category fluency), and genotyping (APOE). Incident dementia was assessed during six years of follow-up. RESULTS: A global model (global cognition, APOE, total brain tissue volume: AUC = 0.920) rendered the highest predictive value for future dementia. Of the models based on specific markers, white matter integrity of the forceps major tract was included in the most predictive model, in combination with perceptual speed and hippocampal volume (AUC = 0.911). CONCLUSION: Assessment of microstructural white matter integrity may improve the early detection of dementia, although the added benefit in this study was relatively small.

Combining Cognitive Markers to Identify Individuals at Increased Dementia Risk: Influence of Modifying Factors and Time to Diagnosis.

OBJECTIVE: We investigated the extent to which combining cognitive markers increases the predictive value for future dementia, when compared to individual markers. Furthermore, we examined whether predictivity of markers differed depending on a range of modifying factors and time to diagnosis. METHOD: Neuropsychological assessment was performed for 2357 participants (60+ years) without dementia from the population-based Swedish National Study on Aging and Care in Kungsholmen. In the main sample analyses, the outcome was dementia at 6 years. In the time-to-diagnosis analyses, a subsample of 407 participants underwent cognitive testing 12, 6, and 3 years before diagnosis, with dementia diagnosis at the 12-year follow-up. RESULTS: Category fluency was the strongest individual predictor of dementia 6 years before diagnosis [area under the curve (AUC) = .903]. The final model included tests of verbal fluency, episodic memory, and perceptual speed (AUC = .913); these three domains were found to be the most predictive across a range of different subgroups. Twelve years before diagnosis, pattern comparison (perceptual speed) was the strongest individual predictor (AUC = .686). However, models 12 years before diagnosis did not show significantly increased predictivity above that of the covariates. CONCLUSIONS: This study shows that combining markers from different cognitive domains leads to increased accuracy in predicting future dementia 6 years later. Markers from the verbal fluency, episodic memory, and perceptual speed domains consistently showed high predictivity across subgroups stratified by age, sex, education, apolipoprotein E ϵ4 status, and dementia type. Predictivity increased closer to diagnosis and showed highest accuracy up to 6 years before a dementia diagnosis. (JINS, 2020, 00, 1-13).

Combining Cognitive, Genetic, and Structural Neuroimaging Markers to Identify Individuals with Increased Dementia Risk.

BACKGROUND: Cognitive and biological markers have shown varying degrees of success in identifying persons who will develop dementia. OBJECTIVE: To evaluate different combinations of cognitive and biological markers and identify prediction models with the highest accuracy for identifying persons with increased dementia risk. METHODS: Neuropsychological assessment, genetic testing (apolipoprotein E -APOE), and structural magnetic resonance imaging (MRI) were performed for 418 older individuals without dementia (60-97 years) from a population-based study (SNAC-K). Participants were followed for six years. RESULTS: Cognitive, genetic, and MRI markers were systematically combined to create prediction models for dementia at six years. The most predictive individual markers were perceptual speed or carrying at least one APOEɛ4 allele (AUC = 0.875). The most predictive model (AUC = 0.924) included variables from all three modalities (category fluency, general knowledge, any ɛ4 allele, hippocampal volume, white matter-hyperintensity volume). CONCLUSION: This study shows that combining markers within and between modalities leads to increased predictivity for future dementia. However, minor increases in predictive value should be weighed against the cost of additional tests in larger-scale screening.