The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals.

Strawbridge RJ, Johnston KJA, Bailey MES, Baldassarre D, Cullen B, Eriksson P, deFaire U, Ferguson A, Gigante B, Giral P, Graham N, Hamsten A, Humphries SE, Kurl S, Lyall DM, Lyall LM, Pell JP, Pirro M, Savonen K, Smit AJ, Tremoli E, Tomainen TP, Veglia F, Ward J, Sennblad B, Smith DJ

Sci Rep 11 (1) 632 [2021-01-12; online 2021-01-12]

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 researchers. For individuals with European ancestry we explored whether genetic variation could identify sub-groups with different metabolic profiles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome-wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank (general population sample) multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Differences between these clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identified three groups of individuals with distinct metabolic profiles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profiles. This work focused on individuals of European ancestry and is unlikely to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with different cardiometabolic profiles.

NGI Uppsala (SNP&SEQ Technology Platform)

National Genomics Infrastructure [Service]

QC bibliography QC xrefs

PubMed 33436761

DOI 10.1038/s41598-020-79964-x

Crossref 10.1038/s41598-020-79964-x

pii: 10.1038/s41598-020-79964-x
pmc: PMC7804422