Background: Drug-induced prolongation of cardiac repolarization limits the treatment with many psychotropic drugs. Recently, the contribution of polygenic variation to the individual duration of the QT interval was identified.
Aims: To explore the interaction between antipsychotic drugs and the individual polygenic influence on the QT interval.
Methods: Retrospective analysis of clinical and genotype data of 804 psychiatric inpatients diagnosed with a psychotic disorder. The individual polygenic influence on the QT interval was calculated according to the method of Arking et al.
Results: Linear regression modelling showed a significant association of the individual polygenic QT interval score (ß = 0.176, < 0.001) and age (ß = 0.139, < 0.001) with the QTc interval corrected according to Fridericia's formula. Sex showed a nominal trend towards significance (ß = 0.064, = 0.064). No association was observed for the number of QT prolonging drugs according to AZCERT taken. Subsample analysis ( = 588) showed a significant association of potassium serum concentrations with the QTc interval (ß = -0.104, = 0.010). Haloperidol serum concentrations were associated with the QTc interval only in single medication analysis ( = 26, ß = 0.101, = 0.004), but not in multivariate regression analysis. No association was observed for aripiprazole, clozapine, quetiapine and perazine, while olanzapine and the sum of risperidone and its metabolite showed a negative association.
Conclusions: Individual genetic factors and age are main determinants of the QT interval. Antipsychotic drug serum concentrations within the therapeutic range contribute to QTc prolongation on an individual level.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8436313 | PMC |
http://dx.doi.org/10.1177/02698811211003477 | DOI Listing |
Alzheimers Dement
January 2025
Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.
Introduction: Traditional multivariate methods for neuroimaging studies overlook the interdependent relationship between brain features. This study addresses this gap by analyzing relative brain volumetric patterns to capture how Alzheimer's disease (AD) and genetics influence brain structure along the disease continuum.
Methods: This study analyzed data from participants across the AD continuum from the Alzheimer's and Families (ALFA) and Alzheimer's Disease Neuroimaging Initiative (ADNI) studies.
Probl Endokrinol (Mosk)
January 2024
Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease.
View Article and Find Full Text PDFBackground: A significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals.
View Article and Find Full Text PDFGenetic prediction of complex traits, enabled by large-scale genomic studies, has created new measures to understand individual genetic predisposition. Polygenic Risk Scores (PRS) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data.
View Article and Find Full Text PDFThe relatively low representation of admixed populations in both discovery and fine-tuning individual-level datasets limits polygenic risk score (PRS) development and equitable clinical translation for admixed populations. Under the assumption that the most informative PRS weight for a homogeneous sample varies linearly in an ancestry continuum space, we introduce a Genetic tance-assisted PRS mbination Pipeline for erse Genetic ncestrie ( ) to interpolate a harmonized PRS for diverse, especially admixed, ancestries, leveraging multiple PRS weights fine-tuned within single-ancestry samples and genetic distance. DiscoDivas treats ancestry as a continuous variable and does not require shifting between different models when calculating PRS for different ancestries.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!