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Background: Poor pulmonary health is increasingly recognized as an important risk factor for accelerated brain ageing and the development of dementia. We report on associations of spirometry with MRI‐based volumetry of the brain in the large community‐based Study of Health in Pomerania Trend (SHIP‐Trend). Method: N=1651 participants of SHIP‐Trend underwent both spirometry and MRI scans of the head. Measurements of forced expiratory volume during first second (FEV1), forced vital capacity (FVC) and relative FEV1 were standardized with respect to age, sex, and body height using the GLI 2012 reference equations. T1‐weighted MRI scans were processed with FreeSurfer 5.3 and total brain volumes (TBV), total gray matter volumes (TGM), total hippocampal volumes (HV) were determined. In addition, we considered the total volume of white matter hyperintensities (WMHV) estimated from T2‐FLAIR images and a recently proposed MRI‐based Alzheimer's disease index (FSAD). Linear regressions of MRI parameters on standardized FEV1, FVC, and relative FEV1, respectively, were performed. Age, sex, intracranial volume, height, smoking status, and presence of diabetes were added as covariates to each model. Effect sizes were assessed by Cohen's f2. Analyses were performed using data from young‐aged (≤ 60y), old‐aged (>60y), and all participants, respectively. Result: FEV1, FVC, and relative FEV1 were significantly positively associated with greater TBV, TGM, and HV in the whole sample. WMHV was significantly negatively associated with FVC and positively associated with relative FEV1. The FSAD index was significantly negatively associated with FEV1, and FVC. Associations with FEV1 and FVC were consistently stronger in old‐aged compared to young‐aged participants. For TBV, HV, WMHV, and FSAD, effect sizes were f2≈0.01. For TGM, effect sizes were f2≈0.05, significantly larger than those of diabetes. No significant associations with relative FEV1 were found in the group of old‐aged participants. Conclusion: Our results suggest that impaired lung function is a risk factor for atrophy of brain tissue. In old‐aged individuals, this seems to be specifically related to restrictive spirometry patterns (i.e. reductions in FEV1 and FVC but not relative FEV1) but not obstructive ones (i.e. reductions in relative FEV1). This work was supported by the Cognitively Healthy Nonagenarians in the Cross‐Cohort Collaboration Consortium (CCC) grant AG059421. [ABSTRACT FROM AUTHOR]

Background: Previous studies showed an inverse correlation between anticholinergic activity from individual medication and cognitive function. Various scales have been developed for scaling the anticholinergic effect of pharmacological substances. The Anticholinergic Cognitive Burden Scale (ACB) and the Anticholinergic Risk Scale (ARS) are the two most commonly used scales for this purpose. Our study evaluated the association of the ACB and the ARS total score with volumes of vulnerable brain structures for cognitive impairment (hippocampus [HIP] and basal forebrain [BF]) in non‐demented participants from a population‐based study. Methods: We analyzed structural MRI and medication (n= 3,316) from participants (aged 20 to 93 years, 52.2% female) of the population‐based Study of Health in Pomerania (SHIP). Anticholinergic burden was obtained from the current medication plan using the ACB and the ARS scales, respectively. We added all scores per participant to an individual total score and evaluated the association with the volume of the HIP and the BF corrected for age, gender, education and total intracranial volume. In addition, we conducted voxel‐based morphometry analysis and compared the results to the volumetric analysis. Result: We identified 1471 medications to be anticholinergic according to the ACB in a total of 706 participants and 446 medications according to the ARS in 188 participants. The volumetric analysis showed a statistically significant inverse association between the ACB and the volume of the HIP (left p=.0003, r=‐.54; right p=.00007, r=‐.59) but no correlation between the ACB total score and the BF volume (p=.070, r=‐.29). The analysis of the volumes of the HIP and the BF and the total ARS score did not show any statistically significant association in these structures (HIP left p=.431, r=+.12, HIP right p=.362, r=+.14, BF p=.055, r=+.28). Conclusion: Several scores with different levels of comprehensiveness are available to calculate the individual anticholinergic burden. Our study showed that the more detailed ACB scoring revealed an association between the anticholinergic burden and the hippocampus whereas the ARS score with a limited number of substances did not show any statistically significant correlations. Further studies in independent samples and clinical follow‐up examinations are needed to better understand the potentials of the different scales. [ABSTRACT FROM AUTHOR]

Background: Sleep is increasingly recognised as a major risk‐factor for cognitive ageing and neurodegenerative disorders such as late‐onset Alzheimer's Disease (LOAD). Poor sleep quality has been linked to impaired cerebral clearance of amyloid‐beta, neurofibrillary tangles and reduced cognitive function. Recent studies have also highlighted a relationship between disturbed sleep and age‐related changes in brain structure. Method: We used ordinary least‐squared regression adjusted for age, gender, BMI, intracranial volume and APOE4‐status to investigate the associations of MRI‐based markers of brain ageing and LOAD‐related brain atrophy patterns (Frenzel et al. 2019; Janowitz et al. 2016 and Weihs et al. 2020) as well as cognitive performance in terms of the immediate and delayed wordlist recollection from the Nuremberg‐Age‐Inventory (NAI) with questionnaire and polysomnography (PSG) based measures of sleep quality in 717 subjects from the large‐scale population‐based Study of Health in Pomerania (SHIP‐Trend). We analysed the percentage of total sleep time (TST) spent in slow‐wave and rapid eye movement (REM) sleep, sleep duration, sleep efficiency and sleep latency as objective sleep quality measures and the Pittsburgh sleep quality index (PSQI) as subjective sleep quality measure. Result: We have identified inverse associations of the percentage of TST spent in slow‐wave and REM sleep with brain atrophy related to ageing and LOAD, which was mainly driven by slow‐wave sleep, with both percentages of TST and duration showing significant inverse associations. Furthermore, there was a non‐linear u‐shaped association between sleep duration and age‐related atrophy patterns. Similar associations were found for the NAI sum score and the NAI delayed recollection sub‐score, but neither were significant after correcting for multiple testing. No associations with the PSQI score were found. Conclusion: We identified associations between PSG based sleep quality markers, but not subjective sleep quality, and brain atrophy related to ageing and LOAD. With no treatment being yet available, improving objective sleep quality, especially slow‐wave sleep, could prove a viable target to slow‐down the process of cognitive decline and reduce the risk of neurodegenerative disorders such as LOAD. [ABSTRACT FROM AUTHOR]