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      Exploring shared genetic bases and causal relationships of schizophrenia and bipolar disorder with 28 cardiovascular and metabolic traits

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          Abstract

          Background

          Cardiovascular diseases represent a major health issue in patients with schizophrenia (SCZ) and bipolar disorder (BD), but the exact nature of cardiometabolic (CM) abnormalities involved and the underlying mechanisms remain unclear. Psychiatric medications are known risk factors, but it is unclear whether there is a connection between the disorders (SCZ/BD) themselves and CM abnormalities.

          Methods

          Using polygenic risk scores and linkage disequilibrium score regression, we investigated the shared genetic bases of SCZ and BD with 28 CM traits. We performed Mendelian randomization (MR) to elucidate causal relationships between the two groups of disorders. The analysis was based on large-scale meta-analyses of genome-wide association studies. We also identified the potential shared genetic variants and inferred the pathways involved.

          Results

          We found tentative polygenic associations of SCZ with glucose metabolism abnormalities, adverse adipokine profiles, increased waist-to-hip ratio and visceral adiposity (false discovery rate or FDR<0.05). However, there was an inverse association with body mass index. For BD, we observed several polygenic associations with favorable CM profiles at FDR<0.05. MR analysis showed that SCZ may be causally linked to raised triglyceride and that lower fasting glucose may be linked to BD. We also identified numerous single nucleotide polymorphisms and pathways shared between SCZ/BD with CM traits, some of which are related to inflammation or the immune system.

          Conclusions

          Our findings suggest that SCZ patients may be genetically predisposed to several CM abnormalities independent of medication side effects. On the other hand, CM abnormalities in BD may be more likely to be secondary. However, the findings require further validation.

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          Most cited references32

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          Impaired Glucose Homeostasis in First-Episode Schizophrenia: A Systematic Review and Meta-analysis.

          Schizophrenia is associated with an increased risk of type 2 diabetes. However, it is not clear whether schizophrenia confers an inherent risk for glucose dysregulation in the absence of the effects of chronic illness and long-term treatment.
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            Transethnic Genetic-Correlation Estimates from Summary Statistics.

            The increasing number of genetic association studies conducted in multiple populations provides an unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics. Here, we have developed a method for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations. This methods takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the genetic correlation was significantly less than 1. Our method is implemented in a Python package called Popcorn.
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              Metabolic syndrome and metabolic abnormalities in bipolar disorder: a meta-analysis of prevalence rates and moderators.

              Patients with bipolar disorder have high levels of cardiovascular disease risk factors. The presence of metabolic syndrome significantly influences future cardiovascular disease morbidity and mortality. The authors sought to clarify the prevalence and moderators of metabolic syndrome in bipolar patients, accounting for subgroup differences. The authors searched MEDLINE, PsycINFO, EMBASE, and CINAHL through April 2012 for research reporting metabolic syndrome prevalence rates in bipolar patients. Medical subject headings "metabolic syndrome" and "bipolar" were used in the title, abstract, or index term fields. Manual searches were conducted using the reference lists from identified articles. The search yielded 81 articles in 37 publications (N=6,983). The overall metabolic syndrome rate was 37.3% (95% confidence interval [CI]=36.1-39.0) using any standardized metabolic syndrome criteria. Compared with general population groups, bipolar patients had higher metabolic syndrome rates (odds ratio=1.98; 95% CI=1.74-2.25). In bipolar patients, older age had a modest effect on the metabolic syndrome rate. The strongest moderator was the region in which the study took place, with the highest rates observed in New Zealand and Australia (64.2% [95% CI=38.3-83.9]) and North America (49.3% [95% CI=29.7-69.3]). Metabolic syndrome was significantly more prevalent in patients currently treated with antipsychotics (45.3% [95% CI=39.6-50.9] than in patients who were antipsychotic free (32.4% [95% CI=27.5-37.4]; odds ratio=1.72 [95% CI=1.24-2.38]). These findings strongly support the claim that patients with bipolar disorder are at high risk for metabolic syndrome and related cardiovascular morbidity and mortality and require regular monitoring and adequate preventive efforts and treatment for cardio-metabolic risk factors. These findings further suggest that the risk of metabolic syndrome is greater in bipolar patients taking prescribed antipsychotic medication.
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                Author and article information

                Journal
                applab
                Psychological Medicine
                Psychol. Med.
                Cambridge University Press (CUP)
                0033-2917
                1469-8978
                June 2019
                July 26 2018
                June 2019
                : 49
                : 08
                : 1286-1298
                Article
                10.1017/S0033291718001812
                30045777
                8bc94194-b87f-40da-82c0-84d0d4539ba2
                © 2019

                https://www.cambridge.org/core/terms

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