Free Recall Episodic Memory Performance Predicts Dementia Ten Years prior to Clinical Diagnosis: Findings from the Betula Longitudinal Study

Boraxbekk, Carl-Johan; Lundquist, Anders; Nordin, Annelie; Nyberg, Lars; Nilsson, Lars‐Göran; Adolfsson, Rolf

doi:10.1159/000381535

Cited by 61 publications

(47 citation statements)

References 51 publications

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“…In the time-to-diagnosis sample, most cognitive tests and combined models were significantly more predictive closer to a diagnosis of dementia. This is consistent with earlier findings which show an increase in predictive value or an increase in magnitude of the effect as the dementia diagnosis approaches (Boraxbekk et al, 2015;Elias et al, 2000;Gomar, Conejero-Goldberg, Davies, & Goldberg, 2014;Rajan et al, 2015). Conceivably, cognitive deficits become more pronounced closer to diagnosis due to a continuing worsening of the underlying disease pathology as the disease progresses.…”

Section: Time-to-diagnosissupporting

confidence: 92%

“…Cognitive deficits are a defining feature of preclinical dementia, observable years or even decades before a clinical diagnosis (Boraxbekk et al, 2015;Elias et al, 2000;Rajan, Wilson, Weuve, Barnes, & Evans, 2015). While research has often focused on episodic memory, a broad range of cognitive domains show deficits during the preclinical phase (Bäckman, Jones, Berger, Laukka, & Small, 2005).…”

Section: Introductionmentioning

confidence: 99%

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

Payton

Rizzuto

Fratiglioni

et al. 2020

J Int Neuropsychol Soc

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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.

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Section: Time-to-diagnosissupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

Payton

Rizzuto

Fratiglioni

et al. 2020

J Int Neuropsychol Soc

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“…The exact reasons for this discrepancy were unclear, because episodic memory measured 10 years prior to clinical diagnosis significantly predicted dementia in the Betula cohort (Boraxbekk et al, 2015). One possible explanation is that the air pollution neurotoxicity observed in the Swedish cohort only accelerated the pathological brain aging process during the clinical stage.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer’s disease

Younan

Petkus

Widaman

et al. 2019

Brain

159

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Evidence suggests exposure to particulate matter with aerodynamic diameter <2.5 μm (PM2.5) may increase the risk for Alzheimer’s disease and related dementias. Whether PM2.5 alters brain structure and accelerates the preclinical neuropsychological processes remains unknown. Early decline of episodic memory is detectable in preclinical Alzheimer’s disease. Therefore, we conducted a longitudinal study to examine whether PM2.5 affects the episodic memory decline, and also explored the potential mediating role of increased neuroanatomic risk of Alzheimer’s disease associated with exposure. Participants included older females (n = 998; aged 73–87) enrolled in both the Women’s Health Initiative Study of Cognitive Aging and the Women’s Health Initiative Memory Study of Magnetic Resonance Imaging, with annual (1999–2010) episodic memory assessment by the California Verbal Learning Test, including measures of immediate free recall/new learning (List A Trials 1–3; List B) and delayed free recall (short- and long-delay), and up to two brain scans (MRI-1: 2005–06; MRI-2: 2009–10). Subjects were assigned Alzheimer’s disease pattern similarity scores (a brain-MRI measured neuroanatomical risk for Alzheimer’s disease), developed by supervised machine learning and validated with data from the Alzheimer’s Disease Neuroimaging Initiative. Based on residential histories and environmental data on air monitoring and simulated atmospheric chemistry, we used a spatiotemporal model to estimate 3-year average PM2.5 exposure preceding MRI-1. In multilevel structural equation models, PM2.5 was associated with greater declines in immediate recall and new learning, but no association was found with decline in delayed-recall or composite scores. For each interquartile increment (2.81 μg/m3) of PM2.5, the annual decline rate was significantly accelerated by 19.3% [95% confidence interval (CI) = 1.9% to 36.2%] for Trials 1–3 and 14.8% (4.4% to 24.9%) for List B performance, adjusting for multiple potential confounders. Long-term PM2.5 exposure was associated with increased Alzheimer’s disease pattern similarity scores, which accounted for 22.6% (95% CI: 1% to 68.9%) and 10.7% (95% CI: 1.0% to 30.3%) of the total adverse PM2.5 effects on Trials 1–3 and List B, respectively. The observed associations remained after excluding incident cases of dementia and stroke during the follow-up, or further adjusting for small-vessel ischaemic disease volumes. Our findings illustrate the continuum of PM2.5 neurotoxicity that contributes to early decline of immediate free recall/new learning at the preclinical stage, which is mediated by progressive atrophy of grey matter indicative of increased Alzheimer’s disease risk, independent of cerebrovascular damage.

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“…Diagnoses were based on the Diagnostic and Statistical Manual of Mental Disorders , Fourth Edition, criteria . The dementia evaluation process has been described previously …”

Section: Methodsmentioning

confidence: 99%

Smell Loss Predicts Mortality Risk Regardless of Dementia Conversion

Ekström

Sjölund

Nordin

et al. 2017

J American Geriatrics Society

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Free Recall Episodic Memory Performance Predicts Dementia Ten Years prior to Clinical Diagnosis: Findings from the Betula Longitudinal Study

Cited by 61 publications

References 51 publications

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

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

Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer’s disease

Smell Loss Predicts Mortality Risk Regardless of Dementia Conversion

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