Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium’s possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response — defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P < 0.05. Li+PGS was positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P = 9.8 × 10−12, R2 = 1.9%) and continuous (P = 6.4 × 10−9, R2 = 2.6%) outcomes. Compared to bipolar patients in the 1st decile of the risk distribution, individuals in the 10th decile had 3.47-fold (95%CI: 2.22-5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P = 3.9 × 10−4, R2 = 0.9%), but not for the continuous outcome (P = 0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGS may be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment.
von Patrick Wagner ; Nils Strodthoff ; Patrick Wurzel ; Arturo Marban ; Sonja Scharf ; Hendrik Schäfer ; Philipp Seegerer ; Andreas Loth ; Sylvia Hartmann ; Frederick Klauschen ; Klaus-Robert Müller ; Wojciech Samek ; Martin-Leo Hansmann
von Kim Grüttner ; Philipp Andreas Hartmann ; Tiemo Fandrey ; Kai Hylla ; Daniel Lorenz ; Stefan Hauck-Stattelmann ; Björn Sander ; Oliver Bringmann ; Wolfgang Nebel ; Wolfgang Rosenstiel
International journal of parallel programming Dordrecht [u.a.] : Springer Science + Business Media B.V., 1972 48(2020), Seite 957-1007 Online-Ressource
Proceedings of the 2015 Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools New York, NY : ACM, 2015 (2015), Article No. 3, insgesamt 6 Seiten 1 Online-Ressource
2014 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS XIV) Piscataway, NJ : IEEE, 2014 (2014), Seite 181-191 III, 386 S.
Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen Rostock : Univ., ITMZ, 2013 (2013), Seite 197-208 [10], 284 S.
Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen Rostock : Univ., ITMZ, 2013 (2013), Seite 147-158 [10], 284 S.
von Kim Grüttner ; Philipp Andreas Hartmann ; Kai Hylla ; Sven Rosinger ; Wolfgang Nebel ; Fernando Herrera ; Eugenio Villar ; Carlo Brandolese ; William Fornaciari ; Gianluca Palermo
