Alcohol use effects on adolescent brain development revealed by simultaneously removing confounding factors, identifying morphometric patterns, and classifying individuals

Group analysis of brain magnetic resonance imaging (MRI) metrics frequently employs generalized additive models (GAM) to remove contributions of confounding factors before identifying cohort specific characteristics. For example, the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) used such an approach to identify effects of alcohol misuse on the developing brain. Here, we hypothesized that considering confounding factors before group analysis removes information relevant for distinguishing adolescents with drinking history from those without. To test this hypothesis, we introduce a machine-learning model that identifies cohort-specific, neuromorphometric patterns by simultaneously training a GAM and generic classifier on macrostructural MRI and microstructural diffusion tensor imaging (DTI) metrics and compare it to more traditional group analysis and machine-learning approaches. Using a baseline NCANDA MR dataset (N = 705), the proposed machine learning approach identified a pattern of eight brain regions unique to adolescents who misuse alcohol. Classifying high-drinking adolescents was more accurate with that pattern than using regions identified with alternative approaches. The findings of the joint model approach thus were (1) impartial to confounding factors; (2) relevant to drinking behaviors; and (3) in concurrence with the alcohol literature. © 2018 The Author(s).1

Anthropometric and genetic determinants of cardiac morphology and function

Background Cardiac structure and function result from complex interactions between genetic and environmental factors. Population-based studies have relied on 2-dimensional cardiovascular magnetic resonance as the gold-standard for phenotyping. However, this technique provides limited global metrics and is insensitive to regional or asymmetric changes in left ventricular (LV) morphology. High-resolution 3-dimensional cardiac magnetic resonance (3D-CMR) with computational quantitative phenotyping, might improve on traditional CMR by enabling the creation of detailed 3D statistical models of the variation in cardiac phenotypes for use in studies of genetic and/or environmental effects on cardiac form or function. Purpose To determine whether 3D-CMR is applicable at scale, and provides methodological and statistical advantages over conventional imaging for large-scale population studies and to apply 3D-CMR to anthropometric and genetic studies of the heart. Methods 1530 volunteers (54.8% females, 74.7% Caucasian, mean age 41.3±13.0 years) without self-reported cardiovascular disease were recruited prospectively to the Digital Heart Project. Using a cardiac atlas-based software, these images were computationally processed and quantitatively analysed. Parameters such as myocardial shape, curvature, wall thickness, relative wall thickness, end-systolic wall stress, fractional wall thickening and ventricular volumes were extracted at over 46,000 points in the model. The relationships between these parameters and systolic blood pressure (SBP), fat mass, lean mass and genetic variationswere analysed using 3D regression models adjusted for body surface area, gender, race, age and multiple testing. Targeted resequencing of titin (TTN), the largest human gene and the commonest genetic cause of dilated cardiomyopathy, was performed in 928 subjects while common variants (~700.000) were genotyped in 1346 subjects. Results Automatically segmented 3D images were more accurate than 2D images at defining cardiac surfaces, resulting in fewer subjects being required to detect a statistically significant 1 mm difference in wall thickness. 3D-CMR enabled the detection of a strong and distinct regionality of the effects of SBP, body composition and genetic variation on the heart. It shows that the precursors of the hypertensive heart phenotype can be traced to healthy normotensives and that different ratios of body composition are associated with particular gender-specific patterns of cardiac remodelling. In 17 asymptomatic subjects with genetic variations associated with dilated cardiomyopathy, early stages of ventricular impairment and wall thinning were identified, which were not apparent by 2D imaging. Conclusions 3D-CMR combined with computational modelling provides high-resolution insight into the earliest stages of heart disease. These methods show promise for population-based studies of the anthropometric, environmental and genetic determinants of LV form and function in health and disease.Open Acces

Physiologische und diagnostische Relevanz der N-Glykosylierung natürlich vorkommender Autoantikörper gegen das Beta-Amyloid Peptid bei der Alzheimer-Krankheit

Die Alzheimer-Krankheit als häufigste neurodegenerative Erkrankung ist ätiopathologisch un-ter anderem durch die Störung der metabolischen Homöostase des Peptids Beta-Amyloid (Aβ42) gekennzeichnet. Interessanterweise finden sich im immunologischen Repertoire des Menschen natürlich vorkommende Autoantikörper wieder, welche eine Aβ42-Reaktivität auf-weisen (nAbs-Aβ42). Ihre Existenz deutet einen die Aβ42-Proteostase betreffenden, regulatori-schen und somit protektiven Mechanismus an, für den die quantitativen und qualitativen Ei-genschaften der Autoantikörper mutmaßlich von entscheidender Bedeutung sind. Die N-Gly-kosylierung als ein prinzipiell entscheidendes qualitatives Attribut von Immunglobulinen, be-einflusst unter anderem deren funktionelle Eigenschaften und könnte auch für die physiolo-gische und somit protektive Funktion der nAbs-Aβ42 von Relevanz sein. Gleichzeitig könnten potentielle Veränderungen der nAbs-Aβ42 Glykosylierung einen entscheidenden Faktor der Pa-thologie darstellen, wodurch sie als diagnostischer Marker zur Identifikation von Alzheimer-Patienten in Frage kommen könnten. Beide Leitgedanken wurden in der vorliegenden Arbeit verfolgt. Für die nAbs konnte zum ei-nen der protektive Effekt auf die Aβ42-Pathologie nachgewiesen und zum anderen eine Ab-hängigkeit dieser physiologischen Wirkung von einer intakten N-Glykosylierung aufgezeigt werden. Diese Resultate liefern zudem wichtige Erkenntnisse für zukünftige therapeutische Strategien auf Basis Aβ42-spezifischer Antikörper, indem sie das Muster und die Komposition der N-Glykane als entscheidende Kriterien für einen wirkungsvollen und nebenwirkungsarmen Ansatz andeuten. Mithilfe der Glykoengineering-Technologie könnten darüber hinaus auch immunologische Prozesse spezifisch moduliert werden, um pathologischen Änderungen der nAbs-Aβ42 Glykosylierung entgegenzuwirken. Tatsächlich waren solche im Rahmen einer Ko-hortenstudie am Fc-Fragment der Autoantikörper von Alzheimer-Patienten nachzuweisen. Auf deren Basis konnte ein generalisiertes lineares Vorhersagemodell entwickelt werden, wel-ches die Zuordnung der Patienten und Probanden mit einer Sensitivität von 95 % und Spezifi-tät von 100 % ermöglichte. Die nAbs-Aβ42 Fc N-Glykosylierung sollte somit als zukünftiger Bio-marker in Betracht gezogen werden, den es gilt, in einer Validierungskohorte zu verifizieren