Forskningsartikler

Abstract

Background: The 1q21.1 distal and 15q11.2 BP1-BP2 CNVs exhibit regional and global brain differences compared to non-carriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intra-individual variability measures can be used to test for regional differences beyond global differences in brain structure.

Methods: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n=30) and duplication (n=27), and 15q11.2 BP1-BP2 deletion (n=170) and duplication (n=243) carriers and matched non-carriers (n=2,350). Regional intra-deviation (RID) scores i.e., the standardized difference between an individual’s regional difference and global difference, were used to test for regional differences that diverge from the global difference.

Results: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate and temporal pole differed less, and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex and temporal pole differed less, and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness.

Conclusion: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 CNVs. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 CNVs, with the potential to increase our understanding of mechanisms involved in altered neurodevelopment.

Bøen, R.,… Persson, K.,…Selbæk, G.,…Sønderby, I.E.

Abstract:

The aim of this study was to assess the diagnostic validity of a deep learning-based method estimating brain age based on magnetic resonance imaging (MRI) and to compare it with volumetrics obtained using NeuroQuant (NQ) in a clinical cohort. Brain age prediction was performed on minimally processed MRI data using deep convolutional neural networks and an independent training set. The brain age gap (difference between chronological and biological age) was calculated, and volumetrics were performed in 110 patients with dementia (Alzheimer’s disease, frontotemporal dementia (FTD), and dementia with Lewy bodies), and 122 with non-dementia (subjective and mild cognitive impairment). Area-under-the-curve (AUC) based on receiver operating characteristics and logistic regression analyses were performed. The mean age was 67.1 (9.5) years and 48.7% (113) were females. The dementia versus non-dementia sensitivity and specificity of the volumetric measures exceeded 80% and yielded higher AUCs compared to BAG. The explained variance of the prediction of diagnostic stage increased when BAG was added to the volumetrics. Further, BAG separated patients with FTD from other dementia etiologies with > 80% sensitivity and specificity. NQ volumetrics outperformed BAG in terms of diagnostic discriminatory power but the two methods provided complementary information, and BAG discriminated FTD from other dementia etiologies.

Karin Persson, Esten H Leonardsen, Trine Holt Edwin, Anne-Brita Knapskog, Gro Gujord Tangen, Geir Selbæk, Thomas Wolfers, Lars T Westlye, Knut Engedal

Background: Malnutrition is common in older adults and is associated with increased morbidity and mortality rates.

Aim: The aim of the study is to describe the prevalence of malnutrition based on low BMI, involuntary weight loss, and reduced food intake, in a Norwegian population of community-dwelling older adults and older adults living in nursing homes.

Methods: This population-based study is part of the fourth wave of the Trøndelag Health Study (HUNT4) and includes participants ≥70 years from the HUNT4 70+ cohort. The HUNT4 70+ cohort consist of 9 930 (response rate 51.2 %) participants. In the current study 8 127 older people had complete dataset for inclusion in the analyses. Participants completed a self-report questionnaire and standardised interviews and clinical assessments at field stations, in participants’ homes or at nursing homes. Malnutrition was defined using the following criteria: low BMI, involuntary weight loss and severely reduced food intake. The standardised prevalence of malnutrition was estimated using inverse probability weighting (IPW) with weights for sex, age and education of the total population in the catchment area of HUNT.

Results: Of the 8 127 included participants, 7 671 (94.4 %) met at field stations, 356 (4.4 %) were examined in their home, and 100 (1.2 %) in nursing homes. In total, 14.3 % of the population were malnourished based on either low BMI, weight loss, or reduced food intake, of which low BMI was the most frequently fulfilled criterion. The prevalence of malnutrition was less common among men than among women (10.1 vs 18.0 %, p < 0.001), also after adjustment for age (OR 0.53, 95 % confidence interval (CI) 0.46-0.60). The prevalence increased gradually with increasing age and the regression analysis adjusted for sex showed that for each year increase in age the prevalence of malnutrition increased with 4.0 % (OR 1.04, 95 % CI 1.03-1.05). The prevalence was higher both among older adults examined in their homes (26.4%) and residents in nursing home (23.6%), as compared to community-dwelling older adults who met at field stations (13.5%).

Conclusion: The prevalence of malnutrition is high in the older population. Special attention on prevention and treatment of malnutrition should be given to older women, the oldest age groups, and care-dependent community-dwelling older adults and nursing home residents.

Marit Kolberg, Ingvild Paur, Yi-Qian Sun, Linda Gjøra, Håvard Kjesbu Skjellegrind, Pernille Thingstad, Bjørn Heine Strand, Geir Selbæk, Tone Natland Fagerhaug & Lene Thoresen