Scientific ReportsGenetic & Genomic MedicinePeer-reviewed journal article
The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals
Rona J. StrawbridgeiD, Keira J.A. JohnstoniD, Mark E.S. BaileyiD, Damiano BaldassarreiD, Breda CulleniD, Per Olof ErikssoniD, Ulf DeFaire, Amy FergusoniD, Bruna GiganteiD, Philippe GiraliD, Nicholas GrahamiD, Anders HamsteniD, Steve Eric HumphriesiD, Sudhir KurliD, Donald M. LyalliD, Laura M. LyalliD, Jill P. PelliD, Matteo PirroiD, Kai P. SavoneniD, Andries Jan SmitiD, Elena TremoliiD, Tomi-Pekka Tomainen, Fabrizio VegliaiD, Joey WardiD, Bengt SennbladiD, Daniel J. SmıthiD
Abstract
Understanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researcher. We explored whether genetic variation could identify individuals with different metabolic profiles. Loci previously associated with schizophrenia, bipolar disorder and major depressive disorder were identified from literature and those overlapping loci genotyped on the Illumina CardioMetabo and Immuno chips (representing cardiometabolic processes and diseases) were selected. In the IMPROVE study (high cardiovascular risk) and UK Biobank (general population) multidimensional scaling was applied to genetic variants implicated in both mental and cardiometabolic illness. Visual inspection of the resulting plots used to identify distinct clusters. Differences between clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both cardiometabolic disease and schizophrenia (but not bipolar or major depressive disorders) identified three groups of individuals with distinct metabolic profiles. The grouping was replicated in UK Biobank, albeit with less distinction between metabolic profiles. This study provides proof of concept that common biology underlying mental and physical illness can identify subsets of individuals with different cardiometabolic profiles.
- organism: human
- assay: GWAS summary statistics
- research focus: benchmarking
- approach: statistical model
- Citations
- 20
- FWCI
- 1.84
- Field percentile
- top 15%
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