Cardiac diseases and sudden cardiac death (SCD) are more prevalent in individuals diagnosed with schizophrenia compared to the general population, with especially coronary artery disease (CAD) as the major cardiovascular cause of death. Antipsychotic medications, genetics, and lifestyle factors may contribute to the increased SCD in individuals with schizophrenia. The role of antipsychotic medications and lifestyle factors have been widely investigated, while the genetic predisposition to inherited cardiac diseases in schizophrenia is poorly understood. In this study, we examined 100 genes associated with inherited cardiomyopathies and cardiac channelopathies in 97 deceased individuals diagnosed with schizophrenia for the prevalence of genetic variants associated with SCD. The deceased individuals had various causes of death and were included in the SURVIVE project, a prospective, autopsy-based study of mentally ill individuals in Denmark. This is the first study of multiple inherited cardiac disease-related genes in deceased individuals with diagnosed schizophrenia to shed light on the genetic predisposition to SCD in individuals with schizophrenia. We found no evidence for an overrepresentation of rare variants with high penetrance in inherited cardiac diseases, following the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG) consensus guidelines. However, we found that the deceased individuals had a statistically significantly increased polygenic burden caused by variants in the investigated heart genes compared to the general population. This indicates that common variants with smaller effects in heart genes may play a role in schizophrenia.
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http://dx.doi.org/10.1007/s00414-021-02595-y | DOI Listing |
Sci Rep
December 2024
Department of Anesthesiology, The First Hospital of Putian City, Putian, China.
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December 2024
Department of Orthopedic Surgery, Yonsei University College of Medicine, Seoul, South Korea.
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December 2024
The Aurum Institute, Parktown, South Africa.
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December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
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December 2024
Department of Cardiac Surgery, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA.
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