Forskningsartikler

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