Forskningsartikler

Abstract:

Objectives: To investigate patterns and correlates of depression among Norwegian older adults (age 70+), 1995-2019, and estimate the number of older adults with depression by 2050.

Design: Population-based cross-sectional study.

Setting and participants: Three surveys of the Trøndelag Health Study (Norway): HUNT2 (1995-96), HUNT3 (2007-08), and HUNT4 (2017-19). 22,822 home dwellers aged 70 + who participated in at least one of the three surveys.

Methods: Depression was defined as scores ≥8 on the depression subscale of the Hospital Anxiety and Depression Scale. Covariates included sex, age, education, marital status, and reported loneliness. Depression prevalence (%) was standardized to the Norwegian population by age, sex, and education for years close to the initial HUNT survey year (1995, 2006, and 2016). Projection of the total number of individuals with depression in the coming decades were estimated. Predictors of depression were analyzed with logistic regression and the potential reduction in depression prevalence by reducing the prevalence of loneliness was estimated.

Results: Standardized depression prevalence decreased from 16.7% (HUNT2) to 14.9% (HUNT3), and 11.5% (HUNT4), and was highest among men, the oldest (85+), the lower-educated, and in earlier surveys (all p < 0.001). Living alone was also associated with higher depression prevalence, but only if loneliness was present. While depression rates are falling, we expect the number of depressed individuals to double by 2050 as the population ages.

Conclusion and implications: Depression rates among adults aged 70 + decreased by 50% from 1995 to 2019, but less so among the oldest old. The rates were highest among single older men. Despite decreasing prevalence, the number of depressed older adults will increase significantly in the future. Given the major individual and societal costs of depression, this trend is alarming for societies preparing for the challenges posed by population aging. This can, however, be addressed by addressing predictors of depression.

Maria Lage Barca, Eivind Aakhus, Ellen Melbye Langballe, Thomas Hansen, Ragnhild Holmberg Aunsmo, Geir Selbæk, Steinar Krokstad, Bjørn Heine Strand

Abstract

Introduction: Loneliness is postulated to be a risk factor for dementia. However, the findings are inconsistent, and long-term studies on this association remain scarce.

Methods: In all, 9389 participants self-reported loneliness in the Trøndelag Health Study (HUNT) in HUNT1 (1984-1986), HUNT2 (1995-1997), and/or HUNT3 (2006-2008) and underwent cognitive assessment in HUNT4 (2017-2019) at age 70 years or older. Logistic regression was employed to analyze the association between the course of loneliness and dementia, with those never lonely as a reference.

Results: In the fully adjusted model, the odds ratio (OR) for persistent loneliness was 1.47 (95% confidence interval [CI] 1.10, 1.95). This attenuated when adjusting for depression (OR 1.28, 95% CI 0.95, 1.72).

Discussion: Persistent loneliness from midlife into older age, as well as becoming lonely, were associated with increased odds of dementia, whereas transient loneliness in midlife was not. These findings underscore the importance of reducing loneliness.

Clinical trial registration: The study was registered with ClinicalTrials.gov (NCT04786561) and is available online .

Highlights: Persistent and incident loneliness was associated with a higher risk of dementia.Transient loneliness was not associated with a higher risk of dementia.Loneliness 11 years before to the cognitive assessment was associated with dementia.Reducing the sense of loneliness might reduce or delay the onset of dementia.

Ragnhild Holmberg Aunsmo, Bjørn Heine Strand, Sverre Bergh, Thomas Hansen, Mika Kivimäki, Sebastian Köhler, Steinar Krokstad, Ellen M Langballe, Gill Livingston, Fiona E Matthews, Geir Selbæk

Abstract:

Background: Mental and neurological conditions have been linked to structural brain variations. However, aside from dementia, the value of brain structural characteristics derived from brain scans for prediction is relatively low. One reason for this limitation is the clinical and biological heterogeneity inherent to such conditions. Recent studies have implicated aberrations in the cerebellum, a relatively understudied brain region, in these clinical conditions.

Methods: Here, we used machine learning to test the value of individual deviations from normative cerebellar development across the lifespan (based on trained data from >27,000 participants) for prediction of autism spectrum disorder (ASD) (n = 317), bipolar disorder (n = 238), schizophrenia (SZ) (n = 195), mild cognitive impairment (n = 122), and Alzheimer’s disease (n = 116); individuals without diagnoses were matched to the clinical cohorts. We applied several atlases and derived median, variance, and percentages of extreme deviations within each region of interest.

Results: The results show that lobular and voxelwise cerebellar data can be used to discriminate reference samples from individuals with ASD and SZ with moderate accuracy (the area under the receiver operating characteristic curves ranged from 0.56 to 0.65). Contributions to these predictive models originated from both anterior and posterior regions of the cerebellum.

Conclusions: Our study highlights the utility of cerebellar normative modeling in predicting ASD and SZ, aided by 4 cerebellar atlases that enhanced the interpretability of the findings.

Milin Kim, Nitin Sharma, Esten H Leonardsen, Saige Rutherford, Geir Selbæk, Karin Persson, Nils Eiel Steen, Olav B Smeland, Torill Ueland, Geneviève Richard, Aikaterina Manoli, Sofie L Valk, Dag Alnæs, Christian F Beckman, Andre F Marquand, Ole A Andreassen, Lars T Westlye, Thomas Wolfers, Torgeir Moberget