Divergent Patterns of Genetic Overlap Between Severe Mental Disorders and Metabolic Markers

Bipolar disorder, major depressive disorder (MDD), and schizophrenia are severe mental disorders and are each associated with poor cardiometabolic health. Mapping genetic relationships of these heritable disorders with blood markers of metabolic activity may uncover biological pathways underlying this important shared clinical feature.

The authors charted genetic overlap of the three disorders, type 2 diabetes, coronary artery disease, and body mass index (BMI) with 249 circulating metabolites through linkage disequilibrium score regression and bivariate Gaussian mixture modeling. Causal relationships and functionally annotated shared genetic variants were estimated, and enrichment across brain and body tissues was investigated.

All three disorders had extensive overlap with the metabolites. The pattern of genetic correlations was similar between MDD, type 2 diabetes, coronary artery disease, and BMI (Spearman's correlation r_s>0.93), opposite in direction to the pattern found for schizophrenia and bipolar disorder (MDD-bipolar disorder r_s=-0.74; MDD-schizophrenia r_s=-0.83). Notably, this genetic divergence contrasted with phenotypic associations, which were similar across all three disorders. The metabolites had widespread, robust causal effects on the disorders and cardiometabolic traits. The authors mapped 1,056 genes shared between the individual disorders and metabolites to disorder-specific processes related to metabolic activity, mitochondrial function, and synaptic processes. These genes were expressed throughout the brain, heart, and liver.

Severe mental disorders have strong associations with metabolites, and MDD has a distinctly different genetic relationship than bipolar disorder and schizophrenia do. The study findings suggest that metabolic pathways are involved in the development of severe mental disorders and can play a central role in disentangling disorder-specific etiologies. The "metabolic psychiatry" approach applied here has high potential to guide development of targeted interventions.