Background: Having accurate, up-to-date information on the epidemiology of mild cognitive impairment (MCI) and dementia is imperative. Objective: To determine the prevalence of MCI and dementia in Norway using data from a large population-based study. Methods: All people 70 + years of age, n = 19,403, in the fourth wave of the Trøndelag Health Study (HUNT4) were invited to participate in the study HUNT4 70 + . Trained health personnel assessed participants using cognitive tests at a field station, at homes, or at their nursing home. Interviewers also completed a structured carer questionnaire in regard to participants suspected of having dementia. Clinical experts made diagnoses according to DSM-5 criteria. We calculated prevalence weighing the data to ensure population representativeness. Results: A total of 9,930 (51.2%) of the possible 19,403 people participated, and 9,663 of these had sufficient information for analysis. Standardized prevalence of dementia and MCI was 14.6% (95% confidence interval (CI) 13.9–15.4) and 35.3% (95% CI 34.3–36.4), respectively. Dementia was more prevalent in women and MCI more prevalent in men. The most prevalent dementia subtype was Alzheimer’s disease (57%). By adding data collected from a study of persons < 70 years in the same region, we estimate that there are 101,118 persons with dementia in Norway in 2020, and this is projected to increase to 236,789 and 380,134 in 2050 and 2100, respectively. Conclusion: We found a higher prevalence of dementia and MCI than most previous studies. The present prevalence and future projections are vital for preparing for future challenges to the healthcare system and the entire society.

Objective: Late-life depression (LLD) is associated with development of different types of dementia. Identification of LLD patients, who will develop cognitive decline, i.e., the early stage of dementia would help to implement interventions earlier. The purpose of this study was to assess whether structural brain magnetic resonance imaging (MRI) in LLD patients can predict mild cognitive impairment (MCI) or dementia 1 year prior to the diagnosis.Methods: LLD patients underwent brain MRI at baseline and repeated clinical assessment after 1-year. Structural brain measurements were obtained using Freesurfer software (v. 5.1) from the T1W brain MRI images. MRI-based Random Forest classifier was used to discriminate between LLD who developed MCI or dementia after 1-year follow-up and cognitively stable LLD. Additionally, a previously established Random Forest model trained on 185 patients with Alzheimer’s disease (AD) vs. 225 cognitively normal elderly from the Alzheimer’s disease Neuroimaging Initiative was tested on the LLD data set (ADNI model).Results: MCI and dementia diagnoses were predicted in LLD patients with 76%/68%/84% accuracy/sensitivity/specificity. Adding the baseline Mini-Mental State Examination (MMSE) scores to the models improved accuracy/sensitivity/specificity to 81%/75%/86%. The best model predicted MCI status alone using MRI and baseline MMSE scores with accuracy/sensitivity/specificity of 89%/85%/90%. The most important region for all the models was right ventral diencephalon, including hypothalamus. Its volume correlated negatively with the number of depressive episodes. ADNI model trained on AD vs. Controls using SV could predict MCI-DEM patients with 67% accuracy.Conclusion: LDD patients developing MCI and dementia can be discriminated from LLD patients remaining cognitively stable with good accuracy based on baseline structural MRI alone. Baseline MMSE score improves prediction accuracy. Ventral diencephalon, including the hypothalamus might play an important role in preservation of cognitive functions in LLD.

Cerebrospinal fluid (CSF) neurofilament light (NFL) is a marker of axonal degeneration. We tested whether CSF NFL levels predict hippocampal atrophy rate in cognitively healthy older adults independently of the established CSF Alzheimer's disease (AD) biomarkers, β-amyloid 1-42, and phosphorylated tau (P-tau). We included 144 participants in a 2-year longitudinal study with baseline CSF measures and 2 magnetic resonance images. Eighty-eight participants had full data available. A subgroup of 36 participants with very low AD risk was also studied. NFL predicted hippocampal atrophy rate independently of age, β-amyloid 1-42, and P-tau. Including NFL, P-tau, and age in the same model, higher NFL and lower P-tau predicted higher hippocampal atrophy (R = 0.20, NFL: β = -0.34; p = 0.003; P-tau: β = 0.27; p = 0.009). The results were upheld in the participants with very low AD risk. NFL predicted neurodegeneration in older adults with very low AD probability. We suggest that factors previously shown to be important for brain degeneration in mild cognitive impairment may also impact changes in normal aging, demonstrating that NFL is likely to indicate AD-independent, age-expected neurodegeneration.

Sleep problems relate to brain changes in aging and disease, but the mechanisms are unknown. Studies suggest a relationship between β-amyloid (Aβ) accumulation and sleep, which is likely augmented by interactions with multiple variables. Here, we tested how different cerebrospinal fluid (CSF) biomarkers for brain pathophysiology, brain atrophy, memory function, and depressive symptoms predicted self-reported sleep patterns in 91 cognitively healthy older adults over a 3-year period. The results showed that CSF levels of total- and phosphorylated (P) tau, and YKL-40-a marker of neuroinflammation/astroglial activation-predicted poor sleep in Aβ positive older adults. Interestingly, although brain atrophy was strongly predictive of poor sleep, the relationships between CSF biomarkers and sleep were completely independent of atrophy. A joint analysis showed that unique variance in sleep was explained by P-tau and the P-tau × Aβ interaction, memory function, depressive symptoms, and brain atrophy. The results demonstrate that sleep relates to a range of different pathophysiological processes, underscoring the importance of understanding its impact on neurocognitive changes in aging and people with increased risk of Alzheimer's disease.