Wiltfang, Jens

Abstract Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental 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=2,367) 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<����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

Objectives Bipolar disorder (BD) with early disease onset is associated with an unfavorable clinical outcome and constitutes a clinically and biologically homogenous subgroup within the heterogeneous BD spectrum. Previous studies have found an accumulation of early age at onset (AAO) in BD families and have therefore hypothesized that there is a larger genetic contribution to the early‐onset cases than to late onset BD. To investigate the genetic background of this subphenotype, we evaluated whether an increased polygenic burden of BD‐ and schizophrenia (SCZ)‐associated risk variants is associated with an earlier AAO in BD patients. Methods A total of 1995 BD type 1 patients from the Consortium of Lithium Genetics (ConLiGen), PsyCourse and Bonn‐Mannheim samples were genotyped and their BD and SCZ polygenic risk scores (PRSs) were calculated using the summary statistics of the Psychiatric Genomics Consortium as a training data set. AAO was either separated into onset groups of clinical interest (childhood and adolescence [≤18 years] vs adulthood [>18 years]) or considered as a continuous measure. The associations between BD‐ and SCZ‐PRSs and AAO were evaluated with regression models. Results BD‐ and SCZ‐PRSs were not significantly associated with age at disease onset. Results remained the same when analyses were stratified by site of recruitment. Conclusions The current study is the largest conducted so far to investigate the association between the cumulative BD and SCZ polygenic risk and AAO in BD patients. The reported negative results suggest that such a polygenic influence, if there is any, is not large, and highlight the importance of conducting further, larger scale studies to obtain more information on the genetic architecture of this clinically relevant phenotype.

Background Different cerebrospinal fluid (CSF) amyloid-beta 1–42 (A β 1–42 ), total Tau (Tau) and Tau phosphorylated at threonine 181 (P-Tau) levels are reported, but currently there is a lack of quality control programmes. The aim of this study was to compare the measurements of these CSF biomarkers, between and within centres. Methods Three CSF-pool samples were distributed to 13 laboratories in 2004 and the same samples were again distributed to 18 laboratories in 2008. In 2004 six laboratories measured A β 1–42 , Tau and P-Tau and seven laboratories measured one or two of these marker(s) by enzyme-linked immunosorbent assays (ELISAs). In 2008, 12 laboratories measured all three markers, three laboratories measured one or two marker(s) by ELISAs and three laboratories measured the markers by Luminex. Results In 2004, the ELISA intercentre coefficients of variance (interCV) were 31%, 21% and 13% for A β 1–42 , Tau and P-Tau, respectively. These were 37%, 16% and 15%, respectively, in 2008. When we restricted the analysis to the Innotest ® ( N = 13) for A β 1–42 , lower interCV were calculated (22%). The centres that participated in both years ( N = 9) showed interCVs of 21%, 15% and 9% and intra-centre coefficients (intraCV) of variance of 25%,18% and 7% in 2008. Conclusions The highest variability was found for A β 1–42 . The variabilities for Tau and P-Tau were lower in both years. The centres that participated in both years showed a high intraCV comparable to their interCV, indicating that there is not only a high variation between but also within centres. Besides a uniform standardization of (pre)analytical procedures, the same assay should be used to decrease the inter/intracentre variation.