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

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 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.

Figure 1

Schematic of the analysis procedure used to identify clusters.

Figure 2

Results of MDS analysis in IMPROVE, using the loci in common between CMD and SCZ with (a) MAF > 1%, (b) MAF > 5% or (c) MAF > 10%; CMD and MDD with (d) MAF > 1%, (e) MAF > 5% or (f) MAF > 10%; CMD and BD with (g) MAF > 1%, (h) MAF > 5% or (i) MAF > 10%. Each data point is an individual therefore the individuals who are closer together are more genetically similar.

Figure 3

Sensitivity testing in UKB1. For comparison, IMPROVE MDS analysis using (a) MAF > 1% and (b) MAF > 10%. MDS analysis in UKB1 using (c) the same post-filtering SNPs as for IMPROVE, (d) the same pre-filtering SNPs with MAF > 1% in UKB1 and (e) the same pre-filtering SNPs with MAF > 10% in UKB1. Each data point is an individual therefore the individuals who are closer together are more genetically similar.

Figure 4

Comparison of the three clusters identified in (a) UKB1 and (b) UKB2 (lower panel). Each data point is an individual therefore the individuals who are closer together are more genetically similar.

Figure 5

Comparison of the three clusters identified in UKB2 in individuals (a) without and (b) with mental illness. Each data point is an individual therefore the individuals who are closer together are more genetically similar.