Zusammenfassung Eine Totalendoprothese (TEP) der Hüfte stellt einen sicheren und sehr häufigen operativen Eingriff dar. Das perioperative Risiko und die Komplikationen während des stationären Aufenthalts werden wesentlich durch Begleiterkrankungen bestimmt. In dieser Studie wurde die Bedeutung eines Diabetes mellitus als Risikofaktor für perioperative Komplikationen sowie Komplikationen nach drei und zwölf Monaten analysiert. In der untersuchten Kohorte von 458 Patienten war das Risiko für perioperative Komplikationen und Komplikationen während des stationären Aufenthalts nach Hüft-TEP beim Vorliegen eines Diabetes mellitus um das Zwei- bis Dreifache gesteigert. Patienten mit Diabetes mellitus wiesen sowohl häufigere chirurgische als auch internistische Komplikationen auf, was im Mittel zu einer um einen Tag verlängerten stationären Verweildauer führte. Nach zwölf Monaten existierten hingegen keine signifikanten Unterschiede zwischen Patienten mit oder ohne Diabetes mellitus. Zusammenfassend stellt ein Diabetes mellitus bei Hüft-TEP einen Risikofaktor für stationäre Komplikationen dar und verlängert die stationäre Verweildauer. Nach zwölf Monaten weisen Patienten mit Diabetes mellitus nach Hüft-TEP jedoch nicht mehr Komplikationen auf als Patienten ohne Diabetes.
Abstract Fluctuations in ovarian hormones influence the risk of depression and Alzheimer's disease, which is twice as high in females. How ovarian hormones affect brain structural plasticity in regions involved in memory and affective cognition, however, remains unclear. Detailed menstrual cycle phenotyping in health may therefore allow for differentiating early processes of cognitive decline from normal aging and offer insights into mechanisms contributing to dementia and depression. We performed longitudinal mapping of medial temporal lobe subregion morphology at 6 timepoints across the menstrual cycle in vivo using a dense-sampling protocol, ultra-high field neuroimaging and individually-derived segmentation analysis in 27 healthy participants (19-34 years). We found positive associations between estradiol and parahippocampal cortex volume, progesterone and subiculum and perirhinal Area 35 volumes, and an estradiol*progesterone interaction with CA1 volume. We confirmed volumetric changes were not driven by hormone-related water or blood-flow changes. We provide open access to the data and analytical pipeline. This resource provides a blueprint for examining shared dynamics of the brain and ovarian function to develop sex-specific strategies for identifying and treating brain disorders that affect memory and cognition.
Introduction: Deep learning models highly accurately predict brain-age from MRI but their explanatory capacity is limited. Explainable A.I. (XAI) methods can identify relevant voxels contributing to model estimates, yet, they do not reveal which biological features these voxels represent. In this study, we closed this gap by relating voxel-based contributions to brain-age estimates, extracted with XAI, to human-interpretable structural features of the aging brain. Methods: To this end, we associated participant-level XAI-based relevance maps extracted from two ensembles of 3D-convolutional neural networks (3D-CNN) that were trained on T1-weighted and fluid attenuated inversion recovery images of 2016 participants (age range 18-82 years), respectively, with regional cortical and subcortical gray matter volume and thickness, perivascular spaces (PVS) and water diffusion-based fractional anisotropy of main white matter tracts.Results: We found that all neuroimaging markers of brain aging, except for PVS, were highly correlated with the XAI-based relevance maps. Overall, the strongest correlation was found between ventricular volume and relevance (r = 0.69), and by feature, temporal-parietal cortical thickness and volume, cerebellar gray matter volume and frontal-occipital white matter tracts showed the strongest correlations with XAI-based relevance. Conclusion: Our ensembles of 3D-CNNs took into account a plethora of known aging processes in the brain to perform age prediction. Some age-associated features like PVS were not consistently considered by the models, and the cerebellum was more important than expected. Taken together, we highlight the ability of end-to-end deep learning models combined with XAI to reveal biologically relevant, multi-feature relationships in the brain.
While recent 'big data' analyses discovered structural brain networks that alter with age and relate to cognitive decline, identifying modifiable factors that prevent these changes remains a major challenge. We therefore aimed to determine the effects of common cardiovascular risk factors on vulnerable gray matter (GM) networks in a large and well-characterized population-based cohort. In 616 healthy elderly (258 women, 60-80 years) of the LIFE-Adult-Study, we assessed the effects of obesity, smoking, blood pressure, markers of glucose and lipid metabolism as well as physical activity on major GM-networks derived using linked independent component analysis. Age, sex, hypertension, diabetes, white matter hyperintensities, education and depression were considered as confounders. Results showed that smoking, higher blood pressure, and higher glycated hemoglobin (HbA1c) were independently associated with lower GM volume and thickness in GM-networks that covered most areas of the neocortex. Higher waist-to-hip ratio was independently associated with lower GM volume in a network of multimodal regions that correlated negatively with age and memory performance. In this large cross-sectional study, we found selective negative associations of smoking, higher blood pressure, higher glucose, and visceral obesity with structural covariance networks, suggesting that reducing these factors could help to delay late-life trajectories of GM aging.
Abstract Social isolation has been suggested to increase the risk to develop cognitive decline. However, our knowledge on causality and neurobiological underpinnings is still limited. In this preregistered analysis, we tested the impact of social isolation on central features of brain and cognitive aging using a longitudinal population-based magnetic resonance imaging (MRI) study. Assaying 1335 cognitively healthy participants (50-80 years old, 659 women) at baseline and 895 participants after ∼6 years follow-up, we found baseline social isolation and change in social isolation to be associated with smaller volumes of the hippocampus, reduced cortical thickness and poorer cognitive functions. Combining advanced neuroimaging outcomes with prevalent lifestyle characteristics from a well-characterized population of middle- to older aged adults, we provide evidence that social isolation contributes to human brain atrophy and cognitive decline. Within-subject effects of social isolation were similar to between-subject effects, indicating an opportunity to reduce dementia risk by promoting social networks.
TMEM18 is the strongest candidate for childhood obesity identified from GWASs, yet as for most GWAS-derived obesity-susceptibility genes, the functional mechanism remains elusive. We here investigate the relevance of TMEM18 for adipose tissue development and obesity. We demonstrate that adipocyte TMEM18 expression is downregulated in children with obesity. Functionally, downregulation of TMEM18 impairs adipocyte formation in zebrafish and in human preadipocytes, indicating that TMEM18 is important for adipocyte differentiation in vivo and in vitro. On the molecular level, TMEM18 activates PPARG, particularly upregulating PPARG1 promoter activity, and this activation is repressed by inflammatory stimuli. The relationship between TMEM18 and PPARG1 is also evident in adipocytes of children and is clinically associated with obesity and adipocyte hypertrophy, inflammation, and insulin resistance. Our findings indicate a role of TMEM18 as an upstream regulator of PPARG signaling driving healthy adipogenesis, which is dysregulated with adipose tissue dysfunction and obesity.
Abstract Objective To test whether elevated blood pressure (BP) relates to grey matter volume (GMV) changes in young adults who had not previously been diagnosed as hypertensive (systolic BP (SBP)/diastolic BP (DBP)≥140/90 mmHg). Methods We associated BP with GMV from structural 3 Tesla T1-weighted MRI of 423 healthy adults between 19-40 years (mean age=27.7±5.3 years, 177 women, SBP/DBP=123.2/73.4±12.2/8.5 mmHg). Data originated from four previously unpublished cross-sectional studies conducted in Leipzig, Germany. We performed voxel-based morphometry on each study separately and combined results in image-based meta-analyses (IBMA) to assess cumulative effects across studies. Resting BP was assigned to one of four categories: (1) SBP<120 and DBP<80 mmHg, (2) SBP 120-129 or DBP 80-84 mmHg, (3) SBP 130-139 or DBP 85-89 mmHg, (4) SBP≥140 or DBP≥90 mmHg. Results IBMA yielded: (a) lower regional GMV was correlated with higher peripheral BP; (b) lower GMV with higher BP when comparing individuals in sub-hypertensive categories 3 and 2, respectively, to those in category 1; (c) lower BP-related GMV was found in regions including hippocampus, amygdala, thalamus, frontal and parietal structures (e.g. precuneus). Conclusions BP≥120/80 mmHg was associated with lower GMV in regions that have previously been related to GM decline in older individuals with manifest hypertension. Our study shows that BP-associated GM alterations emerge continuously across the range of BP and earlier in adulthood than previously assumed. This suggests that treating hypertension or maintaining lower BP in early adulthood might be essential for preventing the pathophysiological cascade of asymptomatic cerebrovascular disease to symptomatic end-organ damage, such as stroke or dementia.
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