Forskningsartikler

Background: A timely diagnosis of dementia can be beneficial for providing good support, treatment, and care, but the diagnostic rate remains unknown and is probably low.

Objective: To determine the dementia diagnostic rate and to describe factors associated with diagnosed dementia.

Methods: This registry linkage study linked information on research-based study diagnoses of all-cause dementia and subtypes of dementias, Alzheimer’s disease, and related dementias, in 1,525 participants from a cross-sectional population-based study (HUNT4 70+) to dementia registry diagnoses in both primary-care and hospital registries. Factors associated with dementia were analyzed with multiple logistic regression.

Results: Among those with research-based dementia study diagnoses in HUNT4 70+, 35.6% had a dementia registry diagnosis in the health registries. The diagnostic rate in registry diagnoses was 19.8% among home-dwellers and 66.0% among nursing home residents. Of those with a study diagnosis of Alzheimer’s disease, 35.8% (95% confidence interval (CI) 32.6-39.0) had a registry diagnosis; for those with a study diagnosis of vascular dementia, the rate was 25.8% (95% CI 19.2-33.3) and for Lewy body dementias and frontotemporal dementia, the diagnosis rate was 63.0% (95% CI 48.7-75.7) and 60.0% (95% CI 43.3-75.1), respectively. Factors associated with having a registry diagnosis included dementia in the family, not being in the youngest or oldest age group, higher education, more severe cognitive decline, and greater need for help with activities of daily living.

Conclusions: Undiagnosed dementia is common, as only one-third of those with dementia are diagnosed. Diagnoses appear to be made at a late stage of dementia.

Linda Gjøra, Bjørn Heine Strand, Sverre Bergh, Ingunn Bosnes, Aud Johannessen, Gill Livingston, Håvard Kjesbu Skjellegrind & Geir Selbæk

Abstract

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.

Esten H. Leonardsen, Karin Persson, Edvard Grødem, Nicola Dinsdale, Till Schellhorn, James M. Roe, Didac Vidal-Piñeiro, Øystein Sørensen, Tobias Kaufmann, Eric Westman, Andre Marquand, Geir Selbæk, Ole A. Andreassen, Thomas Wolfers, Lars T. Westlye & Yunpeng Wang

Background and Objectives
The cognitive reserve hypothesis posits that cognitively stimulating work delays the onset of mild cognitive impairment (MCI) and dementia. However, the effect of occupational cognitive demands across midlife on the risk of these conditions is unclear.
Methods
Using a cohort study design, we evaluated the association between registry-based trajectories of occupational cognitive demands from ages 30–65 years and clinically diagnosed MCI and dementia in participants in the HUNT4 70+ Study (2017–19). Group-based trajectory modeling identified trajectories of occupational cognitive demands, measured by the routine task intensity (RTI) index (lower RTI indicates more cognitively demanding occupation) from the Occupational Information Network. Multinomial regression was implemented to estimate the relative risk ratios (RRRs) of MCI and dementia, after adjusting for age, sex, education, income, baseline hypertension, obesity, diabetes, psychiatric impairment, hearing impairment, loneliness, smoking status, and physical inactivity assessed at HUNT1-2 in 1984–1986 and 1995–1997. To handle missing data, we used inverse probability weighting to account for nonparticipation in cognitive testing and multiple imputation.
Results
Based on longitudinal RTI scores for 305 unique occupations, 4 RTI trajectory groups were identified (n = 7,003, 49.8% women, age range 69–104 years): low RTI (n = 1,431, 20.4%), intermediate-low RTI (n = 1,578, 22.5%), intermediate-high RTI (n = 2,601, 37.1%), and high RTI (n = 1,393, 19.9%). Participants in the high RTI group had a higher risk of MCI (RRR 1.74, 95% CI 1.41–2.14) and dementia (RRR 1.37, 95% CI 1.01–1.86), after adjusting for age, sex, and education compared with participants in the low RTI group. In a sensitivity analysis, controlling for income and baseline health-related factors, the point estimates were not appreciably changed (RRR 1.66, 95% CI 1.35–2.06 for MCI, and RRR 1.31, 95% CI 0.96–1.78 for dementia).
Discussion
People with a history of cognitively stimulating occupations during their 30s, 40s, 50s, and 60s had a lower risk of MCI and dementia older than 70 years, highlighting the importance of occupational cognitive stimulation during midlife for maintaining cognitive function in old age. Further research is required to pinpoint the specific occupational cognitive demands that are most advantageous for maintaining later-life cognitive function.

Trine H Edwin, Asta K Håberg, Ekaterina Zotcheva, Bernt Bratsberg, Astanand Jugessur, Bo Engdahl, Catherine Bowen, Geir Selbæk, Hans-Peter Kohler, Jennifer R Harris, Sarah E Tom, Steinar Krokstad, Teferi Mekonnen, Yaakov Stern, Vegard F Skirbekk, Bjørn H Strand