Greifswald Medical School (German: Medizinische Fakultät der Universität Greifswald) is a division of the University of Greifswald, Germany, consisting of the university's medical school and the adjacent Greifswald University Hospital. Its roots date back to the founding of the university in the year 1456, and it is therefore one of the oldest such institutions in Europe..
The major anxiety disorders (ANX; including generalized anxiety disorder, panic disorder and phobias) are highly prevalent, often onset early and cause substantial global disability. Although distinct in their clinical presentations, they probably represent differential expressions of a dysregulated threat-response system. Here, we present a genome-wide association meta-analysis comprising 122,341 European ancestry ANX cases and 729,881 controls. We identified 58 independent genome-wide significant risk variants and 66 genes with robust biological support. In an independent sample of 1,175,012 self-report ANX cases and 1,956,379 controls, 51 out of the 58 associations replicated. As predicted by twin studies, we found substantial genetic correlation between ANX and depression, neuroticism and other internalizing phenotypes. Follow-up analyses demonstrated enrichment in all major brain regions and highlighted GABAergic signaling as one potential mechanism implicated in ANX genetic risk. These results advance our understanding of the genetic architecture of ANX and prioritize genes for functional follow-up studies.
BACKGROUND AND AIMS:Patients with acute coronary syndromes (ACS) are at high ischaemic risk to which cholesterol, inflammation, and yet-to-be-identified pathways jointly contribute. The junctional protein associated with coronary artery disease (JCAD) drives incident cardiovascular events by acting on coagulation and fibrinolysis. This study aimed to assess whether JCAD serves as a novel marker of or target to address residual risk. METHODS:In the discovery cohort (SPUM-ACS; n = 4787), ACS patients at residual lipid risk [RLR; on-statin LDL cholesterol (LDL-c) ≥70 mg/dL or ≥1.8 mmol/L], residual inflammatory risk [RIR; on-statin high-sensitivity C-reactive protein (hs-CRP) ≥2.0 mg/L], or both (RILR; on-statin LDL-c ≥70 mg/dL and hs-CRP ≥2.0 mg/L) were identified and compared with propensity-score matched controls. Contributions of hs-CRP, LDL-c and JCAD to recurrent major adverse cardiovascular events (MACE) were analysed. In an independent cohort (RISK-PPCI study; n = 496), effects of JCAD on endogenous coagulation and fibrinolysis were gauged, and JCAD-MACE associations were externally validated. RESULTS:At 1 year, patients at RLR, RIR, or RILR were at higher MACE risk as compared to controls [hazard ratio (HR), 1.55, 95% confidence interval (CI) 1.08-2.23; HR 1.80, 95% CI 1.24-2.61; and HR 1.75, 95% CI 1.12-2.75, respectively]. In those at RLR, MACE risk rose with increasing hs-CRP and JCAD, respectively, in uni- (HR per log2 increase, 1.17, 95% CI 1.06-1.30; HR 1.29, 95% CI 1.03-1.62) and multivariable-adjusted models [adjusted (a)HR 1.16, 95% CI 1.03-1.30; aHR 1.27, 95% CI 1.01-1.60]. In those at RIR, MACE risk increased 1.28-fold per log2 increase in JCAD (HR 1.28, 95% CI 1.03-1.59), which prevailed in multivariable-adjusted models (aHR 1.31, 95% CI 1.04-1.65). Similarly, in patients at RILR, MACE risk increased almost linearly with increasing JCAD (HR 1.45, 95% CI 1.09-1.92), independently of potential confounders (aHR 1.47, 95% CI 1.11-1.97). Plasma levels of JCAD correlated positively with proxies of impaired endogenous fibrinolysis, with the JCAD-MACE association being similarly observed in the external validation cohort. CONCLUSIONS:Acute coronary syndrome patients at RLR, RIR, or both are at high ischaemic risk. By modulating coagulation and endogenous fibrinolysis, JCAD represents a promising candidate to address the high residual risk that persists in ACS patients receiving guideline-recommended care. CLINICALTRIALS.GOV IDENTIFIERS:NCT01000701, NCT02562690.
Higher predicted brain age difference has been associated with several psychiatric disorders. Generalized anxiety disorder (GAD) is associated with markers of accelerated aging. In this study, we determined brain predicted age difference (PAD) in individuals with GAD and healthy controls (HC) as well as group differences in PAD variability using voxel-wise structural MRI. The training dataset included 3,511 controls, and the testing dataset included 1,595 individuals with GAD and 4,552 HC from the ENIGMA-Anxiety GAD Working Group. A convolutional neural network model using four input modalities per subject and a model ensemble approach was used to predict brain age. The PAD was then calculated by subtracting chronological age. Model performance was consistent with other image-based brain age prediction models with similar accuracy across the training set (mean absolute error (MAE) = 2.95 years) and HC in the testing set (MAE = 2.94). We found no evidence of accelerated brain aging in individuals with GAD, though we did find evidence for greater variation in PAD for individuals with GAD (Levene's test: W = 442.98, p < .001) and evidence for greater variability in PAD of those with GAD over 25 years of age. No relationships between PAD and clinical or demographic measures were found. To conclude, using large training and testing samples, the study found no significant association between GAD and PAD, although individuals with GAD had greater heterogeneity in brain-predicted age.
The FAIR principles guide data stewardship towards maximizing the value of scientific data while offering a high level of flexibility to accommodate differences in standards and scientific practices. Research communities have developed and implemented domain-specific workflows to make their data FAIR. This work compares the implementation of two externally developed structured FAIRification workflows-a generic workflow and a domain-specific workflow- using the example of metadata captured in diabetes research in Germany and applying the FAIR data maturity model developed by the Research Data Alliance. Interestingly, the implementation of both workflows required similar resources and led us to achieve the same FAIRness rating. We therefore conclude that the adaptations made in the FAIRification workflow for health research data improve efficiency but do not necessarily lead to higher FAIRness scores when applied to core data sets. Based on the results of our workflow comparison, we identified a list of requirements that should be met for the FAIRification of a core data set regardless of the workflow employed. In the future, FAIR data strategies and infrastructure should be planned and implemented as early as possible in the FAIRification journey. It is anticipated that this comparative analysis will help establish standard operating procedures for the FAIRification of core data sets for health studies.
