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- Rosendahl, J, et al.
(författare)
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Genome-wide association study identifies inversion in the CTRB1-CTRB2 locus to modify risk for alcoholic and non-alcoholic chronic pancreatitis
- 2018
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Ingår i: Gut. - : BMJ. - 1468-3288 .- 0017-5749. ; 67:10, s. 1855-1863
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Tidskriftsartikel (refereegranskat)abstract
- Alcohol-related pancreatitis is associated with a disproportionately large number of hospitalisations among GI disorders. Despite its clinical importance, genetic susceptibility to alcoholic chronic pancreatitis (CP) is poorly characterised. To identify risk genes for alcoholic CP and to evaluate their relevance in non-alcoholic CP, we performed a genome-wide association study and functional characterisation of a new pancreatitis locus.Design1959 European alcoholic CP patients and population-based controls from the KORA, LIFE and INCIPE studies (n=4708) as well as chronic alcoholics from the GESGA consortium (n=1332) were screened with Illumina technology. For replication, three European cohorts comprising 1650 patients with non-alcoholic CP and 6695 controls originating from the same countries were used.ResultsWe replicated previously reported risk loci CLDN2-MORC4, CTRC, PRSS1-PRSS2 and SPINK1 in alcoholic CP patients. We identified CTRB1-CTRB2 (chymotrypsin B1 and B2) as a new risk locus with lead single-nucleotide polymorphism (SNP) rs8055167 (OR 1.35, 95% CI 1.23 to 1.6). We found that a 16.6 kb inversion in the CTRB1-CTRB2 locus was in linkage disequilibrium with the CP-associated SNPs and was best tagged by rs8048956. The association was replicated in three independent European non-alcoholic CP cohorts of 1650 patients and 6695 controls (OR 1.62, 95% CI 1.42 to 1.86). The inversion changes the expression ratio of the CTRB1 and CTRB2 isoforms and thereby affects protective trypsinogen degradation and ultimately pancreatitis risk.ConclusionAn inversion in the CTRB1-CTRB2 locus modifies risk for alcoholic and non-alcoholic CP indicating that common pathomechanisms are involved in these inflammatory disorders.
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| 2. |
- Chavanne, A. V., et al.
(författare)
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Individual-Level Prediction of Exposure Therapy Outcome Using Structural and Functional MRI Data in Spider Phobia: A Machine-Learning Study
- 2023
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Ingår i: Depression and Anxiety. - 1091-4269. ; 2023
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Tidskriftsartikel (refereegranskat)abstract
- Machine-learning prediction studies have shown potential to inform treatment stratification, but recent efforts to predict psychotherapy outcomes with clinical routine data have only resulted in moderate prediction accuracies. Neuroimaging data showed promise to predict treatment outcome, but previous prediction attempts have been exploratory and reported small clinical sample sizes. Herein, we aimed to examine the incremental predictive value of neuroimaging data in contrast to clinical and demographic data alone (for which results were previously published), using a two-level multimodal ensemble machine-learning strategy. We used pretreatment structural and task-based fMRI data to predict virtual reality exposure therapy outcome in a bicentric sample of N=190 patients with spider phobia. First, eight 1st-level random forest classifications were conducted using separate data modalities (clinical questionnaire scores and sociodemographic data, cortical thickness and gray matter volumes, functional activation, connectivity, connectivity-derived graph metrics, and BOLD signal variance). Then, the resulting predictions were used to train a 2nd-level classifier that produced a final prediction. No 1st-level or 2nd-level classifier performed above chance level except BOLD signal variance, which showed potential as a contributor to higher-level prediction from multiple regions across the brain (1st-level balanced accuracy=0.63). Overall, neuroimaging data did not provide any incremental accuracy for treatment outcome prediction in patients with spider phobia with respect to clinical and sociodemographic data alone. Thus, we advise caution in the interpretation of prediction performances from small-scale, single-site patient samples. Larger multimodal datasets are needed to further investigate individual-level neuroimaging predictors of therapy response in anxiety disorders.
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